Statistical Matching Methods Research Articles

The impact of startup subsidies on survival, growth, and stakeholder value

ABSTRACT Although the effects of subsidies on the survival, financial performance, and behavioral changes of startups have been extensively studied, their influence on economic value creation and distribution to stakeholders remains underexplored. To address this gap and mitigate publication bias, we examined the entire number of subsidies received by all Spanish startups at founding (51,495) over a decade, analyzing their outcomes 5 years after inception. We employed statistical matching methods controlling for the startups’ founding conditions. The study revealed that subsidies have a positive impact on startups’ growth and provide a significantly higher likelihood of survival; however, no significant effect on profitability was observed. Receiving a subsidy should be considered alongside other factors in explanatory models of business survival. Subsidies are associated with increased economic value creation and distribution to employees but not to the government through taxes. These findings underscore the importance of understanding the long-term consequences of subsidies on stakeholders.

Factors behind the resilience of rural startups

Rural areas are characterized by sparse population, low population density, remoteness, and population aging, which can discourage startups and undermine their financial performance, as the theory of urban agglomeration economies suggests. However, many studies have shown that companies created in rural areas survive longer than those created in urban areas, which seems contradictory. This paper aims to identify the factors that explain this rural resilience. We analyzed all of the startups founded in Spain over 10 years. We used statistical matching methods to test for the effect of the location while controlling for founding conditions and environmental factors. Our study confirmed that few startups are created in rural areas and they grow less, but their survival rate is slightly higher than those established in urban areas. We found that high subsidies, low operating costs, high labor intensity, a slow but low-risk growth pattern, and other foundational conditions and external factors contribute to explaining rural resilience. Nonetheless, additional factors, including the traits of rural entrepreneurs and the lack of alternatives to bankruptcy, as proposed by the entrepreneurial opportunity theory, also matter.

Do thematic funding instruments lead researchers in new directions? Strategic funding priorities and topic switching among British grant recipients

Abstract Research agencies increasingly use thematic funding schemes to steer scientists toward issues of political interest. Funders set strategic priorities and expect scientists to follow the money. Using statistical-matching methods, we paired 10,475 UK-based recipients of thematic grants (with aims proposed by the funding agency) and responsive-mode grants (with aims proposed by researchers) in Physics, Engineering, and Bioscience to investigate the relation between funding-type and shifts in scientists’ research interests. We applied citation-based community detection to create individual time-series of topic distributions based on the grantees’ publications, and used these to estimate funding-related shifts in research focus. Our analysis, which estimates the similarity of the grantees’ research focus before, during and after a grant, suggests that scientists acquiring thematic funding alter their research interests more than comparable scientists funded through responsive-mode schemes. However, recipients of both types of grants tend to revert toward their original research interests when the funding expires. We find no evidence that thematic funding increases the level of diversity in a recipient’s research portfolio. Our study thus highlights an ambiguous link between thematic funding mechanisms and researchers’ orientations.

Measuring The Impact of Special Economic Zone (SEZ) Arun Lhokseumawe on the Sustainability of its Peripheral Area

The impact of special economic zones (SEZ) on the sustainability of their peripheral areas is rarely measured and discussed. This research was conducted to determine the sustainability level of the Arun Lhokseumawe SEZ generated to its surrounding area before and after the exhaustion of its natural gas resources. The statistical matching method and the Moran index used for the analysis involved 14 sustainability variables representing social, economic, and environmental dimensions. The method used includes building the sustainability index using village level data, statistical matching to find evidence of impact, and determination of the Moran index to describe its spatial patterns. Additionally, the index was used to describe the policy implication based on the current SEZ sustainability level. The results show that there is no upward trend in the sustainability index after three years of operation, except for an increase in the SEZ (inside) in 2018 which then declines again in 2021. This figure is lower than before natural gas depletion. It was found that, in 2008, the spatial pattern of the high sustainability index mainly spread to areas outside the Arun Lhokseumawe industrial area. Meanwhile, in 2021, the spatial pattern of a high sustainability index clustered near the Arun Lhokseumawe special economic zone.

Statistical matching using kernel canonical correlation analysis and super-organizing map

The potential to study and improve different aspects of our lives is ever growing thanks to the abundance of data available in today’s modern society. Scientists and researchers often need to analyze data from different sources; the observations, which only share a subset of the variables, cannot always be paired to detect common individuals. This is the case, for example, when the information required to study a certain phenomenon is coming from different sample surveys. Statistical matching is a common practice to combine these data sets. In this paper, we investigate and extend to statistical matching two methods based on Kernel Canonical Correlation Analysis (KCCA) and Super-Organizing Map (Super-OM). These methods are designed to deal with various variable types, sample weights and incompatibilities among categorical variables. In the first case, we use KCCA, a non-linear extension of CCA, to create canonical variables that we can compare in the two data sets. In the second case, Super-OM uses organizing maps to create subgroups of individuals who share the same characteristics. We use the 2017 Belgian Statistics on Income and Living Conditions (SILC) and we compare the performance of the proposed statistical matching methods by means of a cross-validation technique, as if the data were available from two separate sources. The results indicate that our proposed methods are superior to existing methods because they preserve the distribution of generated variables while also providing good predictions. Existing methods typically only achieve one or the other. These new techniques open the door to improving statistical matching in other applications such as medicine, economics, …

Delivering PACE++ curriculum in community settings: Impact of TARA intervention on gender attitudes and dietary practices among adolescent girls in Bihar, India.

Adolescence phase has high intrinsic and instrumental relevance. The Transformative Action for Rural Adolescents intervention delivered PACE++ curriculum with innovations to introduce a) health and nutrition sessions and b) delivery of the content in community settings of rural Bihar. This paper examines impact of the intervention showcasing establishment of intergenerational community connect for empowering and invigorating adolescent girls on gender attitude, empowerment and adolescent health and nutrition. The impact evaluation is based on a two-arm (intervention and comparison groups) cluster randomized controlled design with two rounds of representative cross-sectional surveys. The baseline and endline sample comprised of 2327 and 2033 adolescent girls (15-19 years), respectively. Descriptive statistical, difference-in-differences and propensity score matching methods are used to confirm the program impact. The DID and PSM analyses confirm high significance of impact on gender equity norms, diets and nutritional knowledge and understanding of employee related rights and responsibilities. School-going adolescent girls performed better than those who have discontinued formal education. The intervention showcases the importance of delivering the modified PACE curriculum in rural settings through leveraging community platforms. The findings call for greater policy attention on scaling up of similar initiatives for empowerment and social capital development of adolescent girls.

Consistent and Transferable Force Fields for Statistical Copolymer Systems at the Mesoscale.

The statistical trajectory matching (STM) method was applied successfully to derive coarse grain (CG) models for bulk properties of homopolymers. The extension of the methodology for building CG models for statistical copolymer systems is much more challenging. We present here the strategy for developing CG models for styrene-butadiene-rubber, and we compare the quality of the resulting CG force fields on the structure and thermodynamics at different chemical compositions. The CG models are used through the use of a genuine mesoscopic method called the dissipative particle dynamics method and compared to high-resolution molecular dynamics simulations. We conclude that the STM method is able to produce coarse-grained potentials that are transferable in composition by using only a few reference systems. Additionally, this methodology can be applied on any copolymer system.

Data Fusion for Joining Income and Consumtion Information using Different Donor-Recipient Distance Metrics

Abstract Data fusion describes the method of combining data from (at least) two initially independent data sources to allow for joint analysis of variables which are not jointly observed. The fundamental idea is to base inference on identifying assumptions, and on common variables which provide information that is jointly observed in all the data sources. A popular class of methods dealing with this particular missing-data problem in practice is based on covariate-based nearest neighbour matching, whereas more flexible semi- or even fully parametric approaches seem underrepresented in applied data fusion. In this article we compare two different approaches of nearest neighbour hot deck matching: One, Random Hot Deck, is a variant of the covariate-based matching methods which was proposed by Eurostat, and can be considered as a ’classical’ statistical matching method, whereas the alternative approach is based on Predictive Mean Matching. We discuss results from a simulation study where we deviate from previous analyses of marginal distributions and consider joint distributions of fusion variables instead, and our findings suggest that Predictive Mean Matching tends to outperform Random Hot Deck.

EFFECT OF COMPLEX SAMPLE DESIGN ON DETERMINING COMMON VARIABLES IN STATISTICAL MATCHING METHOD FOR SOCIAL RESEARCH

It is of great importance for researchers to find out different ways of accessing microdata, due to the ever-increasing demand for data and the expectation of reducing the response burden and costs at the same time. In this sense, statistical matching methods have been used extensively to produce new data using existing microdata of surveys and registers recently. It has an increasing application area in social studies such as poverty, deprivation, the effects of newborn on the economic situation of the household, indebtedness and demography, due to the gradual improvement of the micro estimation levels. Selection of matching variables among common variables, at this point, is a critical step in terms of the quality of the microdata to be reached. In the study, while selecting the common variables in order to estimate consumption expenditures by using Statistics on Income and Living Conditions (2018) and Household Budget Survey (2018), weights were added to Hellinger Distance and Spearman2 applications as a new approach. In addition, the effects of design variables (stratum and cluster) were also included in the processes, taking into account the complex structure of both samples. Adding household level weights and design variables to the statistical processes changed the selected or unselected common variables dramatically.

Public Health Risk Assessment and Prevention Based on Big Data.

In order to improve the ability of public health risk assessment in the context of community collaborative prevention and control, a mathematical model of public health risk assessment in the context of community collaborative prevention and control based on the integration and balanced allocation of big data features in the prevention horizon is proposed. The constraint parameter model of public health risk assessment under the background of community collaborative prevention and control is constructed, the method of dynamic feature analysis of joint prevention and control is adopted to realize the dynamic risk point detection of public health risk assessment data and the integration of constraint mechanism related feature points, and the fuzzy dynamic statistical feature matching method is adopted to carry out the adaptive dynamic statistics and resource balanced allocation analysis of public health risk assessment set under the background of community collaborative prevention and control. A public health risk parameter fusion model is established under the background of community collaborative prevention and control, the methods of balanced resource allocation and joint management and control are combined to realize balanced scheduling and prevention area block matching in the process of dynamic parameter estimation of public health risk evaluation data under the background of community collaborative prevention and control, the correlation distribution of public health risk under the background of community collaborative prevention and control is taken as the cost function, and balanced allocation is realized according to the statistical information sampling results of public health risk evaluation data under the background of community collaborative prevention and control. Combined with differential clustering analysis, the data clustering and attribute merging of public health risk assessment under the background of community collaborative prevention and control are realized, and the mathematical modeling optimization of public health risk assessment under the background of community collaborative prevention and control is realized. The simulation results show that this method has good adaptability, high degree of parameter fusion, and strong ability of matching risk prevention areas and balancing resource allocation in the context of community collaborative prevention and control.

Is matching in different situations equally applicable for impact evaluation studies when using observational data

Under certain circumstances, randomisation of interventions may not be applicable in development activities. Quasi-experimental research design such as matching often helps creating counterfactuals for impact evaluations. Although different types of statistical matching methods are available, their relative performance is generally unknown to practitioners. Using five sets of household survey data collected from samples of treatment and comparison groups from four countries in the Hindu Kush Himalaya region, we examine the extent of covariate imbalances before and after matching using four different matching methods. For small samples with enough imbalances in the covariates, the nearest neighbour matching does not perform well, but matching with stratification works better. The performance of radius and kernel matching falls in between nearest neighbour and matching with stratification. We find that the matching is useful when covariates imbalance is high before matching but less useful for sample with relatively balanced covariates before matching.

Southeast Asian protected areas are effective in conserving forest cover and forest carbon stocks compared to unprotected areas

Protected areas aim to conserve nature, ecosystem services, and cultural values; however, they have variable success in doing so under high development pressure. Southeast Asian protected areas faced the highest level of human pressure at the turn of the twenty-first century. To estimate their effectiveness in conserving forest cover and forest carbon stocks for 2000–2018, we used statistical matching methods to control for the non-random location of protected areas, to compare protection against a matched counterfactual. We found Southeast Asian protected areas had three times less forest cover loss than similar landscapes without protection. Protected areas that had completed management reporting using the Management Effectiveness Tracking Tool (METT) conserved significantly more forest cover and forest carbon stocks than those that had not. Management scores were positively associated with the level of carbon emissions avoided, but not the level of forest cover loss avoided. Our study is the first to find that METT scores could predict the level of carbon emissions avoided in protected areas. Given that only 11% of protected areas in Southeast Asia had completed METT surveys, our results illustrate the need to scale-up protected area management effectiveness reporting programs to improve their effectiveness for conserving forests, and for storing and sequestering carbon.

Data integration using statistical matching techniques: A review

In the era of data revolution, availability and presence of data is a huge wealth that has to be utilized. Instead of making new surveys, benefit can be made from data that already exists. As enormous amounts of data become available, it is becoming essential to undertake research that involves integrating data from multiple sources in order to make the best use out of it. Statistical Data Integration (SDI) is the statistical tool for considering this issue. SDI can be used to integrate data files that have common units, and it also allows to merge unrelated files that do not share any common units, depending on the input data. The convenient method of data integration is determined according to the nature of the input data. SDI has two main methods, Record Linkage (RL) and Statistical Matching (SM). SM techniques typically aim to achieve a complete data file from different sources which do not contain the same units. This paper aims at giving a complete overview of existing SM methods, both classical and recent, in order to provide a unified summary of various SM techniques along with their drawbacks. Points for future research are suggested at the end of this paper.

A causal data fusion method for the general exposure and outcome.

With the advent of the big data era, the need to combine multiple individual data sets to draw causal effects arises naturally in many medical and biological applications. Especially each data set cannot measure enough confounders to infer the causal effect of an exposure on an outcome. In this article, we extend the method proposed by a previous study to causal data fusion of more than two data sets without external validation and to a more general (continuous or discrete) exposure and outcome. Theoretically, we obtain the condition for identifiability of exposure effects using multiple individual data sources for the continuous or discrete exposure and outcome. The simulation results show that our proposed causal data fusion method has unbiased causal effect estimate and higher precision than traditional regression, meta-analysis and statistical matching methods. We further apply our method to study the causal effect of BMI on glucose level in individuals with diabetes by combining two data sets. Our method is essential for causal data fusion and provides important insights into the ongoing discourse on the empirical analysis of merging multiple individual data sources.

A Bayesian approach to statistical matching using the national health screening data

The problem of missing data that complicates the data analysis process, is an inevitable phenomenon in various studies. Statistical matching using existing data or information can solve this problem. Many studies have been conducted on statistical matching. This includes linear regression models and nonparametric methods. However, the aforementioned methods may not perform well in small sample problems. This study attempts to address this issue from a Bayesian perspective. In particular, we verify the performance of our Bayesian-based statistical matching method in small sample problems. We use the real observed data from the National Health Screening Data to compare the proposed model with other existing methods.

The impact of strictly protected areas in a deforestation hotspot

AbstractProtected areas are often thought of as a key conservation strategy for avoiding deforestation and retaining biodiversity; therefore, it is crucial to know how effective they are at achieving this purpose. Using a case study from Queensland, Australia, we identified and controlled for bias in allocating strictly protected areas (IUCN Class I and II) and evaluated their impact (in terms of avoiding deforestation) using statistical matching methods. Over the 30 years between 1988 and 2018, approximately 70,481 km2 of native forest was cleared in the study region. Using statistical matching, we estimated that 10.5% (1,447 km2) of Category I and II (strict) protected areas would have been cleared in the absence of protection. Put differently, 89.5% of strictly protected areas are unlikely to have been cleared, even if they were never protected. While previous studies have used statistical matching at a country or state level, we conducted an analysis that allows regional comparison across a single State. Our research indicates that strictly protected areas are marginally effective at preventing deforestation, and this likely due to biases in establishing protected areas on unproductive land.

Assessing the Effectiveness of Student Advice Recommender Agent (SARA): the Case of Automated Personalized Feedback

Greer and Mark’s (2016) paper suggested and reviewed different methods for evaluating the effectiveness of intelligent tutoring systems such as Propensity score matching. The current study aimed at assessing the effectiveness of automated personalized feedback intervention implemented via the Student Advice Recommender Agent (SARA) in a first-year biology class by means of statistical matching and by reviewing and comparing four different statistical matching methods (i.e., exact matching, nearest neighbor matching using the Mahalanobis distance, propensity score matching, and coarsened exact matching). Data from 1026 (73% female and 27% male) students who took a first-year biology course at a Western Canadian university were used. Two different measures for balance assessment of the matched data (i.e., % of balance improvement and standardized bias) were used to choose the best performing statistical matching method. Nearest neighbor matching using the Mahalanobis distance was found to be the most appropriate method for this study and results showed a statistically significant but small treatment effect for the group who received personalized feedback. Research and practical considerations were discussed and suggestions for future research are provided.

Statistical matching techniques in order to plan interventions on socioeconomic weakness: An Italian case

The techniques for integrating data from multiple sources have the objective of identifying records relating to similar or equal units. Moreover, they estimate the joint distribution of several variables observed on different data files and merge information records.As an example, the estimation of the family risk of poverty, based on “objective” indicators (such as income or recourse to indebtedness), is independent from the awareness of those directly involved and therefore is correctly used in order to plan effective interventions against poverty. However, the researchers could usefully investigate also on the “subjective” perceptions of the standard of living and the causes that lead to socioeconomic hardships of families, but often such perceptions are referred to sub-samples of the previous population (or to different samples of it). In this work, we describe a useful model to integrate data between two surveys (Eurostat EU-SILC and Lifestyles survey) through a Statistical Matching method (hot deck distance). The Cluster Analysis is an essential component of the chosen technique of data integration, as well as of the following descriptive analysis.

Kernel canonical correlation analysis for data combination of multiple-source datasets

To investigate the relationship between variables that are not observed simultaneously in the same dataset, “multiple-source datasets” obtained from different individuals or units must be integrated into a “(quasi) single-source dataset”, in which all the relevant variables are observed for the same units. Among various data combination methods, the statistical matching method, frequently used in practical usage in marketing or social sciences, matches units from a certain dataset with similar units from another dataset in terms of the distance of each unit’s values of covariates related to the concerned variables. However, when multiple-source datasets have a large number of covariates, it is difficult to obtain accurate quasi single-source dataset using matching methods, because combination of the covariates’ values becomes complicated and/or it is difficult to deal with the nonlinear relationship between the concerned variables. In this study, we propose a data combination method that combines extension of kernel canonical correlation analysis and statistical matching. This proposed method can estimate canonical variables of a common low-dimensional space that can preserve the relationship between covariates and outcome variables. Using a simulation study and real-world data analysis, we compare our method with existing methods and demonstrate its utility.

European household’s income, consumption and wealth

Poverty indicators purely based on income statistics do not reflect the full picture of household’s economic well-being. Consumption and wealth are two additional key dimensions that determine the economic opportunities of people or material inequalities. We use non-parametric statistical matching methods to join consumption data from the Household Budget Survey to micro data from the European Union Statistics on Income and Living Conditions. In a second step, micro data from the Household Finance and Consumption Survey are joint to produce a common distribution of income, consumption and wealth variables. A variety of different indicators is then produced based on this joint data set, in particular household saving rates. Care has to be taken when interpreting the indicators, since the statistical matching is based on strong assumptions and a limited number of variables common to all of the three original data sets. We are able to show, however, that the assumptions made are justified by the use of strong proxies as matching variables. Thus, the resulting indicators have the potential to contribute to the analysis of inequality patterns and enhance the possibilities of social, and possibly fiscal, policy impact analysis.