Inclusive business and poverty: prospects in the Brazilian context

Inclusive business is a term currently used to explain the organizations that aim to solve social problems with efficiency and financial sustainability by means of market mechanisms. It can be said that inclusive businesses are those targeted at generating employment and income for groups with little or no market mobility, in keeping with the standards of so-called "decent jobs" and in a self-sustaining manner, i.e., generating profit for the enterprises, and establishing relationships with typical business organizations as suppliers of products and services or in the distribution of this type of production. This article discusses the different concepts found in the scientific literature on inclusive businesses. It also analyses data from a survey conducted with the audiences of Social Corporate Responsibility seminars held by FIEMG. This analysis reveals that prospects, risks and idealizations similar to those found in inclusive business theories can also be found among individuals that run social corporate responsibility projects, even if this designation is new for them. The connection between companies and poverty, especially in relation to inclusive businesses, seems full of stumbling blocks and traps in the Brazilian context.

Forensic intelligence framework. Part II: study of the main generic building blocks and challenges through the examples of illicit drugs and false identity documents monitoring

The development of forensic intelligence relies on the expression of suitable models that better represent the contribution of forensic intelligence in relation to the criminal justice system, policing and security. Such models assist in comparing and evaluating methods and new technologies, provide transparency and foster the development of new applications. Interestingly, strong similarities between two separate projects focusing on specific forensic science areas were recently observed. These observations have led to the induction of a general model (Part I) that could guide the use of any forensic science case data in an intelligence perspective. The present article builds upon this general approach by focusing on decisional and organisational issues. The article investigates the comparison process and evaluation system that lay at the heart of the forensic intelligence framework, advocating scientific decision criteria and a structured but flexible and dynamic architecture. These building blocks are crucial and clearly lay within the expertise of forensic scientists. However, it is only part of the problem. Forensic intelligence includes other blocks with their respective interactions, decision points and tensions (e.g. regarding how to guide detection and how to integrate forensic information with other information). Formalising these blocks identifies many questions and potential answers. Addressing these questions is essential for the progress of the discipline. Such a process requires clarifying the role and place of the forensic scientist within the whole process and their relationship to other stakeholders.

Business Exits entail greater future levels of entrepreneurship? An empirical analysis at country level

Using Global Entrepreneurship Monitor data for 41 countries this study investigates the impact of business exit on entrepreneurial activity at the country level. The paper distinguishes between two types of entrepreneurial activity according with the motive to start a new business: entrepreneurs driven by opportunity and necessity motives. The findings indicate that exits have a positive impact on future levels of entrepreneurial activity in a country. For each exit in a given year, a larger proportion of entrepreneurial activity the following year. Moreover, this e ffect turns out to be higher for opportunity entrepreneurs. The findings indicate that both types of entrepreneurial activity rates are influenced by the same factors and in the same direction. However, for some factors we find a di fferential impact on the entrepreneurship. The results show some important implications given that business exit may be overcome when there is a necessity motivation. This has important implications for both researchers and policy makers. JEL codes: L26. Keywords: Entrepreneurship, business exit, social values

Are There Classical Business Cycles?

The aim of this paper is to test formally the classical business cycle hypothesis, using data from industrialized countries for the time period since 1960. The hypothesis is characterized by the view that the cyclical structure in GDP is concentrated in the investment series: fixed investment has typically a long cycle, while the cycle in inventory investment is shorter. To check the robustness of our results, we subject the data for 15 OECD countries to a variety of detrending techniques. While the hypothesis is not confirmed uniformly for all countries, there is a considerably high number for which the data display the predicted pattern. None of the countries shows a pattern which can be interpreted as a clear rejection of the classical hypothesis.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

International business cycles, financial markets and household production

This paper investigates the properties of an international real business cycle model with household production. We show that a model with disturbances to both market and household technologies reproduces the main regularities of the data and improves existing models in matching international consumption, investment and output correlations without irrealistic assumptions on the structure of international financial markets. Sensitivity analysis shows the robustness of the results to alternative specifications of the stochastic processes for the disturbances and to variations of unmeasured parameters within a reasonable range.

Bridging DSGE models and the raw data

A method to estimate DSGE models using the raw data is proposed. The approachlinks the observables to the model counterparts via a flexible specification which doesnot require the model-based component to be solely located at business cycle frequencies,allows the non model-based component to take various time series patterns, andpermits model misspecification. Applying standard data transformations induce biasesin structural estimates and distortions in the policy conclusions. The proposed approachrecovers important model-based features in selected experimental designs. Twowidely discussed issues are used to illustrate its practical use.

Business cycle measurement with some theory

A method to evaluate cyclical models not requiring knowledge of the DGP and the exact specificationof the aggregate decision rules is proposed. We derive robust restrictions in a class of models; use someto identify structural shocks in the data and others to evaluate the class or contrast sub-models. Theapproach has good properties, even in small samples, and when the class of models is misspecified. Themethod is used to sort out the relevance of a certain friction (the presence of rule-of-thumb consumers)in a standard class of models.

Geographical effects on the accuracy of textile trade data:an international approach for 1913

Foreign trade statistics are the main data source to the study of international trade.However its accuracy has been under suspicion since Morgernstern published hisfamous work in 1963. Federico and Tena (1991) have resumed the question arguing thatthey can be useful in an adequate level of aggregation. But the geographical assignmentproblem remains unsolved. This article focuses on the spatial variable through theanalysis of the reliability of textile international data for 1913. A geographical biasarises between export and import series, but because of its quantitative importance it canbe negligible in an international scale.

Relative price riddles in international business cycle theory: Are transport costs the explanation?

We study relative price behavior in an international business cyclemodel with specialization in production, in which a goods marketfriction is introduced through transport costs. The transporttechnology allows for flexible transport costs. We analyze whetherthis extension can account for the striking differences betweentheory and data as far as the moments of terms of trade and realexchange rates are concerned. We find that transport costs increaseboth the volatility of the terms of trade and the volatility of thereal exchange rate. However, unless the transport technology isspecified by a Leontief technology, transport costs do not resolvethe quantitative discrepancies between theory and data. Asurprising result is that transport costs may actually lower thepersistence of the real exchange rate, a finding that is in contrastto much of the emphasis of the empirical literature.

Disability, capacity for work and the business cycle: An international perspective

An important policy issue in recent years concerns the number of people claimingdisability benefits for reasons of incapacity for work. We distinguish between workdisability , which may have its roots in economic and social circumstances, and healthdisability which arises from clear diagnosed medical conditions. Although there is a linkbetween work and health disability, economic conditions, and in particular the businesscycle and variations in the risk of unemployment over time and across localities, mayplay an important part in explaining both the stock of disability benefit claimants andinflows to and outflow from that stock. We employ a variety of cross?country andcountry?specific household panel data sets, as well as administrative data, to testwhether disability benefit claims rise when unemployment is higher, and also toinvestigate the impact of unemployment rates on flows on and off the benefit rolls. Wefind strong evidence that local variations in unemployment have an importantexplanatory role for disability benefit receipt, with higher total enrolments, loweroutflows from rolls and, often, higher inflows into disability rolls in regions and periodsof above?average unemployment. Although general subjective measures of selfreporteddisability and longstanding illness are also positively associated withunemployment rates, inclusion of self?reported health measures does not eliminate thestatistical relationship between unemployment rates and disability benefit receipt;indeed including general measures of health often strengthens that underlyingrelationship. Intriguingly, we also find some evidence from the United Kingdom and theUnited States that the prevalence of self?reported objective specific indicators ofdisability are often pro?cyclical that is, the incidence of specific forms of disability arepro?cyclical whereas claims for disability benefits given specific health conditions arecounter?cyclical. Overall, the analysis suggests that, for a range of countries and datasets, levels of claims for disability benefits are not simply related to changes in theincidence of health disability in the population and are strongly influenced by prevailingeconomic conditions. We discuss the policy implications of these various findings.

Hybrid consumption paths in the attribute space: A model and application with scanner data

This paper presents and estimates a dynamic choice model in the attribute space considering rational consumers. In light of the evidence of several state-dependence patterns, the standard attribute-based model is extended by considering a general utility function where pure inertia and pure variety-seeking behaviors can be explained in the model as particular linear cases. The dynamics of the model are fully characterized by standard dynamic programming techniques. The model presents a stationary consumption pattern that can be inertial, where the consumer only buys one product, or a variety-seeking one, where the consumer shifts among varied products.We run some simulations to analyze the consumption paths out of the steady state. Underthe hybrid utility assumption, the consumer behaves inertially among the unfamiliar brandsfor several periods, eventually switching to a variety-seeking behavior when the stationary levels are approached. An empirical analysis is run using scanner databases for three different product categories: fabric softener, saltine cracker, and catsup. Non-linear specifications provide the best fit of the data, as hybrid functional forms are found in all the product categories for most attributes and segments. These results reveal the statistical superiority of the non-linear structure and confirm the gradual trend to seek variety as the level of familiarity with the purchased items increases.

Are these classical business cycles?

The aim of this paper is to test formally the classical business cyclehypothesis, using data from industrialized countries for the timeperiod since 1960. The hypothesis is characterized by the view that the cyclical structure in GDP is concentrated in the investment series: fixed investment has typically a long cycle, while the cycle in inventory investment is shorter. To check the robustness of our results, we subject the data for 15 OECD countries to a variety of detrending techniques. While the hypothesis is not confirmed uniformly for all countries, there is a considerably high number for which the data display the predicted pattern. None of the countries shows a pattern which can be interpreted as a clear rejection of the classical hypothesis.

Causes of subcontracting: Evidence from panel data on construction firms

This paper examines factors explaining subcontracting decisions in the construction industry. Rather than the more common cross-sectional analyses, we use panel data to evaluate the influence of all relevant variables. We design and use a new index of the closeness to small numbers situations to estimate the extent of hold-up problems. Results show that as specificity grows, firms tend to subcontract less. The opposite happens when output heterogeneity and the use of intangible assets and capabilities increase. Neither temporary shortage of capacity nor geographical dispersion of activities seem to affect the extent of subcontracting. Finally, proxies for uncertainty do not show any clear effect.