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The purpose of this paper is to estimate the impact of one productive development program (PROPYME) in a developing nation like Costa Rica. This program seeks to increase the capacity of small and medium-sized firms (SMEs) to innovate.

Impacts have been estimated assuming that beneficiary firms are trying to maximize their profits and that PROPYME aims to increase these firms productivity. The impacts were measured in terms of three result variables real average wages employment demand and the probability of exporting. A combination of fixed effects and propensity score matching techniques was used in estimations to correct for any selection bias. The authors worked with panel data companies treated and untreated for the period 2001-2011.

PROPYME’s beneficiaries performed better than other firms in terms of labor demand and their probability of exporting. In addition, the dose and the duration of the effects of the treatment (timing effects) are important.

The authors study the impact in ways that go beyond the average treatment effects on the treated (ATT) usually estimated in the existing literature. Specifically, the research focusses on the identification of the timing or dynamic effects (i.e. how long should we wait to see results?) and treatment intensity (dosage effects).

Se estima el impacto de un programa de desarrollo productivo (Propyme) en un país en vías de desarrollo como Costa Rica. El Propyme busca incrementar la capacidad innovadora de las pequeñas y medidas empresas (pymes) costarricenses.

el impacto se ha estimado y evaluado asumiendo que las pymes beneficiaras buscan maximizar sus beneficios y que Propyme se enfoca en incrementar la productividad de esas empresas. El impacto se valoró en función de tres variables: salarios reales medios, empleo demandado y la probabilidad de exportar. Se utilizó una combinación de técnicas de efectos fijos y emparejamiento en las estimaciones con el fin de prevenir sesgos de selección. Se trabajó con un panel de datos, incluyendo empresas tratadas (beneficiarias de Propyme) así como no tratadas para el periodo 2001-2011.

los beneficiarios de Propyme tuvieron mejor desempeño que las restantes empresas en términos de empleo demandado y su posibilidad de exportar. Adicionalmente los efectos dinámicos (dosis y duración) de los tratamientos son importantes.

este artículo evalúa el impacto de una forma que va más allá de lo usual en la literatura por medio de los efectos promedios de los tratamientos sobre los beneficiarios. Esto por cuanto se enfoca en efectos dinámicos como la duración así como la intensidad.

This paper tests the feasibility and empirical implications of a spatial econometric model with a full set of interaction effects and weight matrix defined as an equally weighted group interaction matrix applied to research productivity of individuals. We also elaborate two extensions of this model, namely with group fixed effects and with heteroskedasticity. In our setting, the model with a full set of interaction effects is overparameterised: only the SDM and SDEM specifications produce acceptable results. They imply comparable spillover effects, but by applying a Bayesian approach taken from LeSage (2014), we are able to show that the SDEM specification is more appropriate and thus that colleague interaction effects work through observed and unobserved exogenous characteristics common to researchers within a group.

Information is often available to consumers through their social networks. Focusing on dairy consumers in India, this paper aims to present evidence of peer effects in consumers’ attitudes towards various food safety attributes and food safety practices.

Unobserved individual heterogeneities are crucial confounders in the identification of social (endogenous) effects. The identification is based on exploiting within-consumer variation across different aspects of attitude (or practices) related to food safety.

This paper uses a novel identification strategy that allows for average effects across attributes and practices to be estimated. Using the strategy, though this paper cannot estimate endogenous effects in each attribute or practice, this paper is able to identify such effects averaged over attributes or practices.

Cross-sectional study, caste affiliation is not defined at the right level of granularity.

The results suggest that information campaigns aimed at creating awareness about food safety can have social multiplier effects, and this also translates into changes in the practices followed to mitigate food safety risks.

In health-related awareness and practices, there are well-established cases of multiplier effects. The most significant example of this is the Pulse Polio campaign in India, where an awareness drives through social multiplier effects had such a significant impact that in 2012 India was declared polio-free. Perhaps, a similar campaign in matters related to food safety could be very fruitful.

The methodology and the issue are unique. Little exists in assessing social networks in the context of food safety.

The purpose of this paper is to test empirically whether there exist spillover externalities in job satisfaction, i.e., to test whether individual-level job satisfaction is affected by the aggregate job satisfaction level in a certain labor market environment.

The authors use a linear-in-means model of social interactions in the empirical analysis. The authors develop an original strategy, motivated by the hierarchical models of social processes, to identify the parameters of interest. BHPS and WERS datasets are used to perform the estimations both at the establishment and local labor market levels.

The authors find that one standard deviation increase in aggregate job satisfaction leads to a 0.42 standard deviation increase in individual-level job satisfaction at the workplace level and a 0.15 standard deviation increase in individual-level job satisfaction at the local labor market level. In other words, the authors report that statistically significant job satisfaction spillovers exist both at the establishment level and local labor market level; and, the former being approximately three times larger than the latter.

First, this is the first paper in the literature estimating spillover effects in job satisfaction. Second, the authors show that the degree of these spillover externalities may change at different aggregation levels. Finally, motivated by the hierarchical models of social processes, the author develop an original econometric identification strategy.

Employment rates fell dramatically between March and April 2020 as the initial shock of the COVID-19 pandemic reverberated through the US labor market. This paper uses data from the CPS Basic Monthly Files to document that the employment decline was particularly severe for immigrants. Historically, immigrant men are more likely to work than native men. The pandemic-related labor market shock eliminated the immigrant employment advantage. After this initial precipitous drop, however, the employment recovery through June 2021 was much stronger for immigrants, and particularly for undocumented immigrants. The steep drop in immigrant employment at the start of the pandemic occurred partly because immigrants were less likely to work in jobs that could be performed remotely and suffered disproportionate employment losses as only workers with remotable skills were able to continue working from home. The stronger employment recovery of undocumented immigrants, relative to that experienced by natives or legal immigrants, is mostly explained by the fact that undocumented workers were not eligible for the generous unemployment insurance (UI) benefits offered to workers during the pandemic.

This paper aims to study low adoption of modern technology for pearl millet in Rajasthan, India, from the perspective of social networks. The state has the lowest adoption of modern pearl millet seeds among Indian states. . In particular, this paper tries to identify the limitations of channels with endogenous effects, thereby limiting large-scale adoption of modern varieties that would require social multipliers.

Defining the network/reference groups in terms of social identity and geographical proximity, this paper utilizes the intensity of interaction with different network nodes to identify the presence of endogenous effects. In particular, this paper uses the interaction of intensity of social exchange with the group level adoptions to establish the presence of endogenous effects. With adequate controls, greater intensity of interaction having a bearing on technology choice can only happen when there exists social learning (endogenous effect) and cannot be associated with other forms of social effects (namely, exogenous and correlated effects).

This paper finds evidence for the existence of endogenous social effects in adoption but largely from exclusionary channels. A comprehensively mapped network is used with its intensity to explain the extremely low rate of adoption. Only close-knit networks that, with social fragmentation, limit benefits to few, affect adoption significantly. The non-functionality of less exclusionary information sources and services can be a factor underlying low adoption.

The main limitation of the study is inability to control for unobserved individual heterogeneity because of the cross-sectional nature of data. Further, although an extensive mapping of individual networks has been done, it still cannot be guaranteed to be exhaustive.

With fragmentation, large-scale adoption programs would require networks, sources of information and services that are less exclusionary. Based on the survey data, media and non-religious organizations play a focal role here in the adoption of modern technology. This finding is extremely crucial for policy, as these channels comprise direct policy levers in a fragmented society like India. Indeed, several government programs in India have relied on these channels to run large-scale adoption programs. Their ineffectiveness could be a prime factor for such limited dissemination of technology in Rajasthan.

In different settings, social fragmentation could be an important factor determining technology adoption outcomes. The evolving consensus in the literature based on several studies is that ethnic fragmentation has potentially negative consequences on macro-economic performance (Alesina and Tabellini, 1989 and Collier, 2000). In the literature on technology adoption, the role of fractionalization is somewhat under-studied. With fragmentation, there can be significant micro-level impacts (for instance, low technology adoption of a crop) if channels that are inclusive are not well developed. The finding that channels like extension services, media or organizations are not effective in determining choice of technology does not mean that they should not be tapped. The empirical findings suggest that, in their current form in the state of Rajasthan, the roles played by these are limited. The policy implications would be to develop these systems in a way that there is a greater uptake. Recall that less than 4 per cent of the respondents got information on seeds from media sources, an extremely low number. There is certainly scope for increasing the outreach of these channels that are much more important for spread of agricultural technology in a fragmented society.

This paper is an attempt to come up with an empirical strategy to mitigate the issues related to reflection problem. In the cross-sectional data itself, we use the interaction of group choices with intensity of interaction within the group to introduce a non-linearity that tries to bypass the identification issues as in reflection problem. This method of introducing non-linearity in cross-sectional data is a novel attempt to achieve identification of endogenous effects.

This chapter analyzes the extent to which more experienced employers, arbitrators, and attorneys fare better in securities industry arbitration. Although studies into experience have identified a so-called repeat-player effect on outcomes, I argue that more nuanced considerations of experience are required.

I empirically analyze all employment arbitration awards from the securities system’s inception through 2008. I separate experience into two categories (between- and within-group effects) and run hybrid random- and fixed-effects regressions modeling increasing employer, attorney, and arbitrator experience on arbitration outcomes.

I find that between-group experience affects awards but that within-group experience is nonsignificant, except in civil rights cases. This implies that so-called repeat players gain an advantage over inexperienced players due to their entity-specific characteristics, not necessarily by learning to use the system to their advantage. I conclude that, although the securities arbitration system suffers from power imbalances, there is little evidence of systemic exploitation by firms.

Prior studies into employment arbitration are limited both by their definitions of experience and by their methodological approaches. I overcome these issues by employing a novel methodological approach to measure between- and within-entity experience, which adds a more multifaceted and nuanced framework to the literature than the common repeat-player versus single-player dichotomy.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

The decline in cigarette consumption within developed countries makes the exploitation of new markets particularly important to tobacco companies. UK data on exports is used to show that, by splitting the sample into three groups, income and prices have significant but different effects on cigarette import demand according to country type. Additionally the extent to which firms vary prices according to these internal circumstances in an effort to increase total consumption is examined. Stresses that, while exports to newly industrializing and oil exporting countries have risen most dramatically, markets in Less Developed Countries are also a fertile source of future sales and may provide a focus for future policy co‐ordination.