scholarly journals Do Suspensions Affect Student Outcomes?

2018 ◽  
Vol 41 (1) ◽  
pp. 34-62 ◽  
Author(s):  
Johanna Lacoe ◽  
Matthew P. Steinberg
Keyword(s):  
School District ◽  
Reading Achievement ◽  
Panel Data ◽  
Policy Change ◽  
Student Outcomes ◽  
Instrumental Variable ◽  
Fixed Effects ◽  
Unintended Consequences ◽  
School District Of Philadelphia

Discipline reformers claim that suspensions negatively affect suspended students, while others suggest reforms have unintended consequences for peers. Using student panel data from the School District of Philadelphia, we implement student fixed effects and instrumental variable (IV) strategies to examine the consequences of suspensions for offending students and their peers. A suspension decreases math and reading achievement for suspended students. The effects are robust to IV estimates leveraging a district-wide policy change in suspension use. Suspensions are more salient for students who personally experience suspension than for their peers. Exposure to suspensions for more serious misconduct has very small, negative spillovers onto peer achievement, but does not change peer absences.

Retirement and memory in Europe

2015 ◽  
Vol 36 (7) ◽  
pp. 1434-1458 ◽  
Author(s):  
LAURA BIANCHINI ◽  
MARGHERITA BORELLA
Keyword(s):  
Panel Data ◽  
Instrumental Variable ◽  
Fixed Effects ◽  
Word Recall ◽  
Individual Heterogeneity ◽  
Variable Technique ◽  
Data Set ◽  
Memory Measure ◽  
Measure Word ◽  
Positive Effect

ABSTRACTWe investigate the effect of retirement on memory using the Survey on Health, Ageing and Retirement in Europe (SHARE). The availability of a panel data-set allows individual heterogeneity to be controlled for when estimating the effect of transitions into retirement on a commonly employed memory measure, word recall. We control for endogeneity of the retirement decision applying an instrumental variable technique to our fixed-effects transformation. Our main finding is that, conditional on the average non-linear memory age path of the typical individual, time spent in retirement has a positive effect on word recall.

Unintended Consequences: The Impact of Proposition 2½ Overrides on School Segregation in Massachusetts

2014 ◽  
Vol 9 (4) ◽  
pp. 481-514 ◽  
Author(s):  
Jeffrey Zabel
Keyword(s):  
School District ◽  
Panel Data ◽  
School Districts ◽  
Public Services ◽  
Unintended Consequences ◽  
Unintended Consequence ◽  
School Segregation ◽  
The Impact

I investigate a possible unintended consequence of Proposition 2½ override behavior—that it led to increased segregation in school districts in Massachusetts. This can occur because richer, low-minority towns tend to have more successful override votes that attract similar households with relatively high demands for public services who can afford to pay for them. To evaluate this hypothesis, I collect panel data on override behavior from 1982 to 2012 and merge this with data on school district enrollments and other district- and town-level characteristics. I find evidence that passing overrides earmarked for schools results in a significant decrease in the percent of nonwhite students enrolled in Massachusetts school districts. This happens in districts with below-average nonwhite school enrollments, and hence increases segregation.

scholarly journals Instrumental Variable Quantile Regression of Spatial Dynamic Durbin Panel Data Model with Fixed Effects

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3261
Author(s):  
Danqing Chen ◽  
Jianbao Chen ◽  
Shuangshuang Li
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Data Model ◽  
Instrumental Variable ◽  
Fixed Effects ◽  
Finite Sample ◽  
Spatial Dynamic ◽  
Spatial Weights ◽  
Disturbance Term ◽  
Instrumental Variable Quantile Regression

This paper studies a quantile regression spatial dynamic Durbin panel data (SDDPD) model with fixed effects. Conventional fixed effects estimators of quantile regression specification are usually biased in the presentation of lagged response variables in spatial and time as regressors. To reduce this bias, we propose the instrumental variable quantile regression (IVQR) estimator with lagged covariates in spatial and time as instruments. Under some regular conditions, the consistency and asymptotic normalityof the estimators are derived. Monte Carlo simulations show that our estimators not only perform well in finite sample cases at different quantiles but also have robustness for different spatial weights matrices and for different disturbance term distributions. The proposed method is used to analyze the influencing factors of international tourism foreign exchange earnings of 31 provinces in China from 2011 to 2017.

Using information from singletons in fixed-effects estimation: xtfesing

2020 ◽  
Vol 20 (4) ◽  
pp. 965-975
Author(s):  
Laura Magazzini ◽  
Randolph Luca Bruno ◽  
Marco Stampini
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Panel Data Regression ◽  
Data Regression ◽  
Method Of Moments Estimator ◽  
Fixed Effects Estimation

In this article, we describe the xtfesing command. The command implements a generalized method of moments estimator that allows exploiting singleton information in fixed-effects panel-data regression as in Bruno, Magazzini, and Stampini (2020, Economics Letters 186: Article 108519).

Fitting partially linear functional-coefficient panel-data models with Stata

2020 ◽  
Vol 20 (4) ◽  
pp. 976-998
Author(s):  
Kerui Du ◽  
Yonghui Zhang ◽  
Qiankun Zhou
Keyword(s):  
Monte Carlo ◽  
Panel Data ◽  
Fixed Effects ◽  
Linear Functional ◽  
Semiparametric Estimation ◽  
Data Models ◽  
Panel Data Models ◽  
Panel Models ◽  
Partially Linear ◽  
Functional Coefficient

In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah ( Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators.

Determinants of Corporate Cash Holdings

2017 ◽  
Vol 18 (2) ◽  
pp. 416-427 ◽  
Author(s):  
Yogesh Maheshwari ◽  
K.T. Vigneswara Rao
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Capital Expenditure ◽  
Cash Holdings ◽  
Working Capital ◽  
Financial Policies ◽  
Panel Data Regression ◽  
Strong Positive Relationship ◽  
Development Expenditure ◽  
Corporate Cash Holdings

This article aims at examining the financial determinants of corporate cash holdings. The study employs panel data regression method. It uses the fixed-effects method based on Hausman test results for the estimation of panel data model. This study has implications that are beneficial for the business managers to have a better understanding and appreciation of the role and importance of the determinants of corporate cash holdings in formulating and evaluating the corporate financial policies. The results of the study indicate a strong positive relationship between cash holdings and cash flow, dividend payment, market-to-book ratio, net debt issuance and net equity issuance of the sample firms. It is also found that the cash holdings of these firms are negatively affected by net working capital, leverage, research and development expenditure as well as capital expenditure of the firm. The article will help researchers as well as managers to understand as to what motivates the firms to hold cash, given the fact that despite being often termed as a non-earning asset, firms generally hold more cash than their normal working capital requirement.

scholarly journals TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS

2013 ◽  
Vol 29 (6) ◽  
pp. 1079-1135 ◽  
Author(s):  
Liangjun Su ◽  
Qihui Chen
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Data Models ◽  
Development Economic ◽  
Strong Mixing ◽  
Panel Data Models ◽  
Finite Sample ◽  
Test Statistic ◽  
Lm Test ◽  
Interactive Fixed Effects

This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions that allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional strong mixing. To improve the finite-sample performance of the test, we also propose a bootstrap procedure to obtain the bootstrap p-values and justify its validity. Monte Carlo simulations suggest that the test has correct size and satisfactory power. We apply our test to study the Organization for Economic Cooperation and Development economic growth model.

Assessing the Impact of the Factors in the Digital Transformation on the Regional Economic Growth

REGIONOLOGY ◽  
2021 ◽  
Vol 29 (3) ◽  
pp. 486-510
Author(s):  
Tatyana V. Mirolyubova ◽  
Marina V. Radionova
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Fixed Effects ◽  
Digital Transformation ◽  
Regional Economic Growth ◽  
Regional Economic ◽  
The Russian Federation ◽  
Regional Product ◽  
The Impact ◽  
Regions Of Russia

Introduction. The scientific problem under consideration is of particular relevance due to the need to assess the impact of the factors in the digital transformation of the regional economy and in the economic growth on the economic development of the regions of the Russian Federation. Based on the research conducted, the article presents an econometric assessment of the dependence of the level of the gross regional product per capita in the regions of Russia on such factors as digital labor and digital capital. Materials and Methods. The authors analyzed panel data from the Federal State Statistics Service covering 87 regions of Russia for the period from 2010 to 2018. The research methodology is based on the use of the Cobb–Douglas production function, statistical and correlation data analysis, as well as on econometric methods for studying panel data. Results. To analyze the impact of the digital transformation of the economy on the regional economic growth of the regions of Russia, various models based on panel data have been considered, such as the pooled model, fixed effects models, random effects models, as well as time-varying effects models using dummy variables. Based on statistical criteria, the best model has been chosen and conclusions have been drawn about the nature of the impact of the digital transformation indicators on the gross regional product per capita in the regions of Russia. Discussion and Conclusion. The results of econometric modeling have demonstrated that digital factors in economic growth (digital labor, digital capital), along with common factors in economic growth (labor and capital), affect the regional economic growth. According to the regional data for the period from 2010 to 2018, the time fixed effects model has proved to be the best model of the impact of the factors in economic growth and digital transformation on the economic development of the regions of the Russian Federation. The research results can be used when developing a public policy aimed at stimulating the digital transformation of the regional economy.

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