omitted variables
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Author(s):  
MARIA BALLESTEROS-SOLA ◽  
GERMAN OSORIO-NOVELA

We conducted an exploratory multi-case study of female, necessity micro-entrepreneurs in developing contexts to partially validate an existing theoretical model and identify relevant omitted variables. Using a sample of eight female, necessity entrepreneurs in Tijuana (Mexico), we were able to challenge the established pull-push binary framework in entrepreneurship as well as the linear entrepreneurial process. Our analysis suggests that motivations, family embeddedness and gender identity are critical factors impacting the female micro-venture creation process in developing contexts. We summarize our theoretical findings in a new process model that accounts for macro, meso and micro factors, offering contributions to the scholarship on female entrepreneurship in developing contexts.


2021 ◽  
Vol 2021 (1) ◽  
pp. 90-98
Author(s):  
Teguh Ammar Taqiyyuddin ◽  
Muhammad Irfan Rizki

Permasalahan yang ada di setiap negara khususnya negara berkembang termasuk Indonesia adalah kemiskinan. Program dalam mengentaskan kemiskinan merupakan pokok tujuan dari Sustainable Development Goals (SDGs). Jawa Barat yang merupakan salah satu provinsi dengan jumlah penduduk miskin terbanyak perlu mengatasi permasalah tersebut seperti yang tertuang dalam RPJMD. Dalam hal ini pemerintah seringkali menentukan pembangunan dengan memprioritaskan pembangunan ekonomi pada daerah perkotaan ataupun pusat perekonomian yang mengakibatkan daerah lainnya tertinggal dan kemiskinan menjadi tidak merata. Hal tersebut tentunya memperlihatkan faktor yang berhubungan dengan ekonomi diduga terdapat aspek spasial sehingga harus menggunakan spasial lag variabel prediktor sebagai prediktor variabel, selain itu kemiskinan merupakan masalah multidimensial sehingga banyak faktor yang mempengaruhi tingkat kemiskinan tidak dimasukkan ke dalam pemodelan. Variabel prediktor yang tidak dimasukkan ke dalam pemodelan dinamakan omitted variables. Berdasarkan permasalahan itu, dalam mengetahui faktor-faktor kemiskinan di Jawa Barat diperlukan suatu pendekatan yang mampu mengakomodasi lag spasial prediktor variabel dan error model yang berkorelasi spasial, serta mampu mengatasi bias taksiran akibat omitted variables. Maka dalam penelitian ini dilakukan pendekatan model regresi spasial Durbin Error Model. Pembobot spasial yang digunakan yaitu queen contiguity. Berdasarkan penelitian ini didapatkan bahwa variabel Indeks Pembanguna Manusia (IPM) dan persentase penduduk berpengaruh terhadap tingkat kemiskinan di Provinisi Jawa Barat, dengan nilai R-Square sebesar 98%. Maka hasil tersebut diharapkan dapat menjadi pertimbangan bagi pemerintah Jawa Barat untuk menanggulangi masalah kemiskinan dalam upaya mencapai tujuan pertama SDGs yaitu tanpa kemiskinan.


2021 ◽  
Vol 14 (10) ◽  
pp. 467
Author(s):  
Jonathan Leightner ◽  
Tomoo Inoue ◽  
Pierre Lafaye de Micheaux

There are many real-world situations in which complex interacting forces are best described by a series of equations. Traditional regression approaches to these situations involve modeling and estimating each individual equation (producing estimates of “partial derivatives”) and then solving the entire system for reduced form relationships (“total derivatives”). We examine three estimation methods that produce “total derivative estimates” without having to model and estimate each separate equation. These methods produce a unique total derivative estimate for every observation, where the differences in these estimates are produced by omitted variables. A plot of these estimates over time shows how the estimated relationship has evolved over time due to omitted variables. A moving 95% confidence interval (constructed like a moving average) means that there is only a five percent chance that the next total derivative would lie outside that confidence interval if the recent variability of omitted variables does not increase. Simulations show that two of these methods produce much less error than ignoring the omitted variables problem does when the importance of omitted variables noticeably exceeds random error. In an example, the spread rate of COVID-19 is estimated for Brazil, Europe, South Africa, the UK, and the USA.


2021 ◽  
Vol 40 (9) ◽  
pp. 646-654
Author(s):  
Henning Hoeber

When inversions use incorrectly specified models, the estimated least-squares model parameters are biased. Their expected values are not the true underlying quantitative parameters being estimated. This means the least-squares model parameters cannot be compared to the equivalent values from forward modeling. In addition, the bias propagates into other quantities, such as elastic reflectivities in amplitude variation with offset (AVO) analysis. I give an outline of the framework to analyze bias, provided by the theory of omitted variable bias (OVB). I use OVB to calculate exactly the bias due to model misspecification in linearized isotropic two-term AVO. The resulting equations can be used to forward model unbiased AVO quantities, using the least-squares fit results, the weights given by OVB analysis, and the omitted variables. I show how uncertainty due to bias propagates into derived quantities, such as the χ-angle and elastic reflectivity expressions. The result can be used to build tables of unique relative rock property relationships for any AVO model, which replace the unbiased, forward-model results.


2021 ◽  
Vol 9 (3) ◽  
pp. 154-164
Author(s):  
Sofia Curdumi Pendley ◽  
Mark Jennings VanLandingham ◽  
Nhu Ngoc Pham ◽  
Mai Do

We explore the current state of the principal literature relevant to resilience and vulnerability within and among communities of forced migrants. We highlight strengths, gaps, and weaknesses in these literatures, utilizing a case study to illustrate the importance of what we deem to be essential omitted variables. We make recommendations for moving these literatures—and their associated underlying conceptual frameworks—forward.


2021 ◽  
Vol 111 (8) ◽  
pp. 2697-2735
Author(s):  
Amy Finkelstein ◽  
Matthew Gentzkow ◽  
Heidi Williams

We estimate the effect of current location on elderly mortality by analyzing outcomes of movers in the Medicare population. We control for movers’ origin locations as well as a rich vector of pre-move health measures. We also develop a novel strategy to adjust for remaining unobservables, using the correlation of residual mortality with movers’ origins to gauge the importance of omitted variables. We estimate substantial effects of current location. Moving from a tenth to a ninetieth percentile location would increase life expectancy at age 65 by 1.1 years, and equalizing location effects would reduce cross-sectional variation in life expectancy by 15 percent. Places with favorable life expectancy effects tend to have higher quality and quantity of health care, less extreme climates, lower crime rates, and higher socioeconomic status. (JEL H51, I1, I11)


2021 ◽  
Author(s):  
Christina Hansen Edwards ◽  
Gunnhild Åberge Vie ◽  
Christina Hansen Edwards

Abstract Background: Past studies have found associations between obesity and healthcare costs, however, these studies have suffered from bias due to omitted variables, reverse causality, and omitted variables. Methods: We used genetic variants related to body mass index (BMI) as instruments for BMI; thereby exploiting the natural randomization of genetic variants that occurs at conception. We used data on measured height and weight, genetic information, and sociodemographic factors from the Nord-Trøndelag Health Studies (HUNT), and individual-level registry data on healthcare costs, educational level, registration status, and biological relatives. We studied associations between BMI and general practitioner (GP)-, specialist-, and total healthcare costs in the Norwegian setting using instrumental variable (IV) regressions, and compared our findings with effect estimates from ordinary least squares (OLS) regressions. The sensitivity of our findings to underlying IV-assumptions was explored using two-sample Mendelian randomization methods, non-linear analyses, sex-, healthcare provider-, and age-specific analyses, within-family analyses, and outlier removal. We also conducted power calculations to assess the likelihood of detecting an effect given our sample 60 786 individuals.Results: We found that increased BMI resulted in significantly higher GP costs; however, the IV-based effect estimate was smaller than the OLS-based estimate. We found no evidence of an association between BMI and specialist or total healthcare costs. Conclusions: The effect of obesity on GP- and specialist costs may have been overestimated in previous studies.


2021 ◽  
pp. 016237372110305
Author(s):  
Ann Mantil

Interdistrict desegregation programs, which provide opportunities for urban children of color to attend suburban schools, are a potential means of addressing persistent racial inequalities in educational opportunities and outcomes. These voluntary programs offer a test of whether nonresident students can leverage the resources and social capital available at high-performing suburban schools to improve their educational outcomes. In the first impact study of Boston’s long-running program, I find large differences in the adjusted high-school graduation and college enrollment rates of applicants referred to a suburban district, compared with observably similar applicants who were not referred. The college effect is due to enrollment in 4-year institutions and does not vary by gender. Estimates are robust to adjustments for remaining omitted variables bias.


PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253291
Author(s):  
Liang Frank Shao

Multicollinearity widely exists in empirical studies, which leads to imprecise estimation and even endogeneity when omitted variables are correlated with any regressors. We apply an innovative strategy, different from the usual tools (instrumental variable, ridge regression, and least absolute shrinkage and selection operator), to estimate the robust determinants of income distribution. We transform panel data into (quasi-) cross-sectional data by removing country and time effects from the data so that all variables become zero mean and orthogonal to the country dummies and time variable, and multicollinearity becomes very low or even disappears with the quasi-cross sectional data in any specifications regardless of country dummies and time variable being included or not. Our contribution is threefold. First, we build a general method to address the multicollinearity issue in panel data, which is to isolate the common contents of correlated variables and ensures robust estimates in different specifications (dynamic or static specifications) and estimators (within- or between-effects estimators). Second, we find no evidence for the Kuznets hypothesis within and across countries; investment is economically and statistically the most robust determinant of income inequality; meanwhile, labor income share shows robustly and consistently positive effects on income inequality, which challenges the related literature. Last, simulations with our estimates show that the total marginal effects of development (regarding GDP, capital stock and investment) on income inequality are very likely to be positive within and between countries except that the impacts on middle-60% and top-quintile income shares are not so likely to increase income inequality across countries.


2021 ◽  
Author(s):  
Farnaz Pourzand

<p><b>Three manuscripts form the basis of this dissertation exploring the effect of drought and climate on agriculture in New Zealand. The first manuscript examines the effects of droughts on agricultural profitability and farms' business performance indicators across dairy and sheep/beef land-uses in New Zealand. This study applies a fixed effect panel regression model using financial and agricultural data at the firm level from Statistics New Zealand's Longitudinal Business Database (LBD) over 2007-2016. The analysis shows that, on average, a recent drought increases revenue and profit from dairy farming. </b></p><p>The second manuscript explores regional differences in the impacts of drought events in New Zealand between 2007-2016. Dramatically different climatic conditions across New Zealand regions motivated this work. The study finds that Waikato and Taranaki's dairy farms – the main dairy producers- positively affected by drought event. This effect is potentially associated with drought‐induced higher milk prices. The positive impacts of drought are no longer identifiable once the model control for milk prices. Whereas sheep/beef farms' gross income and profit were negatively affected by droughts across most sheep/beef farming regions. The analysis also reports that there is no relationship between the persistent impact of drought events and farms' income and profits, on average, over three years. </p><p>The third manuscript estimates the Ricardian approach to examine how climate differences affect farmland values in New Zealand. This study applies the spatial first differences (SFD) method that compares climate differences to land value differences between adjacent neighbours to eliminate the omitted variables bias. This work estimates the effect of climate on overall rural land-uses and various land-uses between 1993 and 2018. The SFD estimation shows that warmer conditions are associated with higher capital values. There is also a positive relationship between farmland values and dryer soils. These relationships are likely due to causal effects of factors tied to climate such as agricultural productivity, the value of land improvements (tied to climate), and amenity values associated with residential uses. </p>


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