First Term GPA is an Important Predictor for Retention. But What Factors Help Us Predict First Term GPA?

Previous research indicates first term GPA is one of the best predictors of first year retention (Zhang, 2006; Luo, Williams, & Vieweg, 2007; Hosch, 2008; Cambra & Stanley, 2008; and Colorado State University, 2013). The next logical question is what factors are the best predictors of first term GPA? Knowing what factors best predict first term GPA might provide valuable insights and predictive analytics for improving first term GPA and consequential first year retention outcomes.

Data, Variables and Statistical Analysis

To gain an understanding of variables that are associated with first term GPA I exploit a dataset from a midwest community college. The variables of the analysis are summarized in the following table. Information for all variables were obtained for 10,218 students.

Variables of the analysis

The variable, accel, requires more elaboration. It captures high school students who have earned college credit. Programs that provide this opportunity are known as dual enrollment, concurrent enrollment, Tech Prep, and STEM.

Multiple regression was chosen as the statistical tool of choice as the dependent variable, first term GPA (ftgpa), is a continuous variable and the predictors are a mixture of categorical and continuous variables. Multiple regression provides an opportunity to assess the association of each predictor on ftgpa.

Multiple Regression Output and Interpretation

The following represents the output from regressing ftgpa on the seven predictors.

First Term GPA regression output

First, we observe the full model with the seven predictors is statistically significant (Prob F > 0.0000).  However, gender is not statistically significant so we will drop the variable ‘female’ and recast the regression with six predictors, resulting in the following output.

First term GPA regression output without female

The full model as well as the six predictors are statistically significant. The model accounts for 32% of the variance in first term GPA. The regression coefficients may be interpreted in the following manner:

  • White students have a .10 first term GPA advantage when compared to non-whites, all other variables set to their means.
  • Accelerated students achieve a .45 first term GPA advantage over non-accelerated students, ceteris paribus.
  • First generation students exhibit a -.56 (lower) first term GPA compared to students with backgrounds from families with previous experience in higher education, controlling for all other covariates.
  • A one unit increase in fall term credit hours is associated with a .08 increase in first term GPA, holding all other predictors to their means. Thus an additional 3 credit course would be expected to yield a .24 increase in first term GPA, controlling for all other independent variables.
  • A one unit increase in high school GPA is associated with a .69 increase in first term GPA, holding constant all other variables.
  • Each additional unit increase in high school graduation year decreases students first term GPA by a factor of .06. (Older students achieve a higher first term GPA than more recent high school graduates.)

It’s useful to observe the association of predictors with first term GPA at specified levels of the predictors, controlling for the influence of the other covariates. The following graphs provide that visual opportunity.

I. Predicting First Term GPA by Race and High School GPA

Prediciting first term gpa By Race and High School GPA
II. Predicting First Term GPA by Year of High School Graduation

Prediciting first term gpa by year of high school graduation
III. Predicting First Term GPA by Acceleration and High School GPA

Prediciting first term gpa by Acceleration and High School GPA

IV. Predicting First Term GPA by First Generation and High School GPA

Prediciting first term gpa By First Generation and High School GPA

V. Predicting First Term GPA by First Term Credits and Race

Prediciting first term gpa By First Term Credits and Race
VI. Predicting First Term GPA by First Generation and First Term Credits

Prediciting first term gpa By First Generation and First Term Credits
VII. Predicting First Term GPA by First Term Credits and Acceleration

Prediciting first term gpa By First Term Credits and Acceleration

Predictive Analytics

With data in hand and the completed multiple regression analysis it is possible to predict each student’s first-term GPA. For example, the following table is the outcome associated with asking my statistical software program (Stata) to list the first five students with predicted first term GPAs less than 2.0.

predictive analytics - first term gpa

All five are white, none of them have been accelerated, three are first generation college students, all are part-time students (based on their credit hours), all have high school GPAs under 2.0 and all are older than traditional aged college students.

With predictive analytics community colleges can exploit this strategic information to design intervention programs which improve mission fulfillment, student outcomes and the financial position of the college.

Implications for Open Door Community Colleges

Open door community colleges admit individuals who have a high school diploma or GED certificate. ( I hasten to add that I have years of data indicating students who graduate from community colleges and then transfer to a four-year college do as well or better than cohorts who entered the university as freshmen.)

What can community colleges do to improve first term GPA with the expectation that such improvements yield positive retention outcomes? How can they accomplish this objective without compromising their missions?

  • Use “predictive analytics” to identify high-risk students and sub-groups unlikely to achieve a 2.0 GPA during the first term.
  • Provide financial support for long-term intervention programs. In short, institutionalize strategic initiatives to improve first term GPA and consequential retention.
  • Use the above information for “targeted”student advising.
  • Develop initiatives  which promote credit momentum. Educate students on the cost implications for not graduating on‐time.  Recall, an additional 3 credit course is associated on average with a .24 increase in first term GPA, holding all other predictors to their means. This strategy appears to be especially productive for accelerated students. See graph VII.
  • Improve recruitment and admissions of accelerated students. (Recall accelerated students have a .45 first term GPA advantage over non-accelerated students.)
  • Increase the number of students participating in accelerated programs (dual enrollment, concurrent enrollment, Tech Prep, and STEM.)
  • Improve recruitment, admissions and financial aid for older students. (Older students achieve a higher first term GPA than more recent high school graduates.)
  • While community colleges are open-door institutions there’s certainly no mission conflict in recruiting students with higher GPAs in high school.
  • Consider offering a “summer bridge” program for at-risk students.
  • Provide staff development opportunities for the “freshman year experience.”
  • “Measure it to improve it.” Adopt CQI strategies and use the information to improve first term GPAs.
  • Establish a “First-Term GPA & Retention Committee,” reporting directly to the President’s Council. Establish clear and measurable yearly objectives, which involve the buy-in of all stakeholders. Annual progress reports, including both sub-group and aggregate analyses, should be routinely available for all faculty and staff, but most importantly, acted upon.
  • Never forget the power of faculty/staff reaching out to students.
  • Celebrate success — with students! If we desire higher first term GPAs and consequential retention outcomes then we need to make those goals explicit and provide appropriate recognition, rewards and status on the basis of achieving prescribed goals.

Reference

Cambra, Ron & Stanley, John.  (2008). Access to Success: Leading Indicators Workgroup. The University of Hawaiʻi at Mānoa.

Colorado State University. (2013). Early Indicators of Student Progress and Success. Office of Institutional Research.

Hosch, Braden J. (2008).  Institutional and Student Characteristics that Predict Graduation and Retention Rates.  Paper presented at the North East Association for Institutional Research Annual Meeting, Providence, Rhode Island.

Luo, M., Williams, J. E., Vieweg, B. (2008). Transitioning transfer students: Interactive factors that influence first-year retention. College and University, 83(2), 8-19.

Zhang, Biao. (2006). Factors Affecting 1st Year Transfer Retention at a Midwest University.

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Caterpillar’s Offshore Profit Shifting

Subcommittee exposes Caterpillar offshore profit shifting
Source: U.S. Senate Permanent Subcommittee on Investigations

Caterpillar Inc., an American manufacturing icon, used a wholly owned Swiss affiliate to shift $8 billion in profits from the United States to Switzerland to take advantage of a special 4 to 6 percent corporate tax rate it negotiated with the Swiss government and defer or avoid paying $2.4 billion in U.S. taxes to date, a new report from Sen. Carl Levin, the chairman of the U.S. Senate Permanent Subcommittee on Investigations shows.

“Caterpillar is an American success story that produces phenomenal industrial machines, but it is also a member of the corporate profit-shifting club that has shifted billions of dollars in profits offshore to avoid paying U.S. taxes,” Levin said. “Caterpillar paid over $55 million for a Swiss tax strategy that has so far enabled it to avoid paying $2.4 billion in U.S. taxes. That tax strategy depends on the company making the case that its parts business is run out of Switzerland instead of the U.S. so it can justify sending 85 percent or more of the parts profits to Geneva. Well, I’m not buying that story.”

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Study: Odds That Global Warming Is Due to Natural Factors – Slim to None

Via Newswise — An analysis of temperature data since 1500 all but rules out the possibility that global warming in the industrial era is just a natural fluctuation in the earth’s climate, according to a new study by McGill University physics professor Shaun Lovejoy.

The study, published online April 6 in the journal Climate Dynamics, represents a new approach to the question of whether global warming in the industrial era has been caused largely by man-made emissions from the burning of fossil fuels. Rather than using complex computer models to estimate the effects of greenhouse-gas emissions, Lovejoy examines historical data to assess the competing hypothesis: that warming over the past century is due to natural long-term variations in temperature.

“This study will be a blow to any remaining climate-change deniers,” Lovejoy says. “Their two most convincing arguments – that the warming is natural in origin, and that the computer models are wrong – are either directly contradicted by this analysis, or simply do not apply to it.” Continue reading–>

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Did 5 Million People Lose Their Insurance as a Result of ObamaCare?

The debate goes on. Did 5 million people lose their insurance as Republicans claim?

Jonathan Cohn, looking at the Rand Corporation’s study of ObamaCare, which estimates 9.3 million Americans now have health insurance, finds encouraging indicators that the 5 million figure may be grossly over estimated. Cohn explains:

“Republicans and their supporters frequently say that 5 million people “lost” health insurance, … Sometimes Republicans and their supporters imply that these people actually ended up uninsured. But if Rand is right—and, again, there’s no way to be sure right now—then it would appear most people who lost their old plans were able to get new ones instead. That’s consistent with anecdotal reports from insurers.”

“In short, not all the 4.8 million people who lost their old coverage are worse off. It’s not even clear that a majority of them are. That’s one more reason the case against Obamacare may be even weaker than you’ve heard.”

Stay tuned. Evidence will eventually reveal the actual facts.

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Diminishing Corporate Taxes and Critical Shortfalls in State Budgets

Via Center for Effective Government:

WASHINGTON, March 27, 2014—A new report published today by Center for Effective Government and National People’s Action (see PDF) uncovers how the shrinking corporate tax base is driving critical budget shortfalls and service cuts at the state and federal level. The report outlines exactly how much revenue has been lost due to a precipitous decline in corporate income tax rates and an explosion of loopholes. The report shows that since the recession, corporate income tax revenues have shrunk considerably, despite soaring profits, leaving individuals to pick up the slack.

Reference

Center for Effective Government. (2014). The Disappearing Corporate Tax Base.

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Why Inequality Matters: Associations of Inequality with Social Problems

In this post I present a number of graphs depicting the association of inequality with a number of social problems. In part this study was motivated by the work of epidemiologists, Richard Wilkinson and Kate Pickett, documented in their book, “The Spirit Level: Why Greater Equality Makes Society Stronger.” The difference in their work and mine is that Wilkinson and Pickett’s unit of analysis was countries whereas I use U.S. counties as the unit of analysis.

In the graphs that follow each social problem is represented on the vertical axis with the Gini coefficient, capturing inequality, on the horizontal axis. Greater levels of inequality are represented by higher Gini coefficients. Each graph is a scatter plot (each dot represents a county) and a line of best fit is provided with confidence intervals shaded in gray.  At the end of this post is an appendix with a correlation table of the Gini coefficient with each social problem and a reference to the data sources. Recall the caveat: correlations do not necessarily represent causation.

Association of Inequality with Injury Death Rate

Association of Inequality with High School Graduation Rate

Association of Inequality with Air Pollution

Association of Inequality with Teen Birth Rate

Association of Inequality with Percent Violent Crime Rate

Association of Inequality with Percent Children in Poverty

Association of Inequality with Percent Diabetic

Association of Inequality with Percent Excessive Drinkers

Association of Inequality with Percent Long Commute & Drive Alone

Association of Inequality with Percent Alcohol Impaired Driving Deaths

Association of Inequality with Percent Single Households

Association of Inequality with Percent Uninsured

Association of Inequality with Percent Unemployed

Association of Inequality with 'Percent Some College'

Association of Inequality with Percent Smokers

Association of Inequality with Percent Low Birth Weight

Association of Inequality with Percent Severe Housing

Association of Inequality with Percent Physically Inactive

Association of Inequality with Percent Obesity

Association of Inequality with Percent No Emotional Support

Appendix

Correlations
with Gini Coefficient
Variable Gini
pct_obese 0.0705*
pct_smoke 0.0907*
pct_low_birth _rate 0.4454*
pct__phyically_inactive 0.1633*
pct_excessive_drinkers -0.2220*
pct_alcohol_impaired_deaths -0.0478
teen_birth_rate_x100 0.3583*
pct_uninsured 0.3779*
pct_diabetic -0.1492*
high_school_grad_rate -0.2621*
pct_some_college -0.1921*
pct_unemployed 0.2424*
pct_children_poverty 0.5346*
pct_no_emotional_support 0.3450*
pct_sningle_household 0.4379*
violent_crime_rate 0.3512*
injury_death_rate 0.1798*
air_poll -0.0538
pct_severe_housing_problems 0.3834*
pct_long_commute_drive_alone -0.1089*

* Represents .05 level of significance

Data sources:

1) County Health Rankings from Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute , and
2) Gini coefficients (2009-11) from American Community Survey.

Related posts

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Democracy Does Cause Growth

Abstract

We provide evidence that democracy has a significant and robust positive effect on GDP. Our empirical strategy relies on a dichotomous measure of democracy coded from several sources to reduce measurement error and controls for country fixed effects and the rich dynamics of GDP, which otherwise confound the effect of democracy on economic growth. Our baseline results use a linear model for GDP dynamics estimated using either a standard within estimator or various different Generalized Method of Moments estimators, and show that democratizations increase GDP per capita by about 20% in the long run. These results are confirmed when we use a semiparametric propensity score matching estimator to control for GDP dynamics. We also obtain similar results using regional waves of democratizations and reversals to instrument for country democracy. Our results suggest that democracy increases future GDP by encouraging investment, increasing schooling, inducing economic reforms, improving public good provision, and reducing social unrest. We find little support for the view that democracy is a constraint on economic growth for less developed economies.

Reference

Acemoglu, Daron, et al. (2014). Democracy Does Cause Growth. National Bureau of Economic Research. NBER Working Paper No. 20004.

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States Most & Least Dependent on the Federal Government and Worst States to be a Taxpayer

Via WalletHub:

I. States Most & Least Dependent on the Federal Government
Scroll over a state with your cursor to identify its rank.

II. Worst States to be a Taxpayer

WalletHub

III. Correlation Analysis: Tax Rates with Dependency on Federal Government

WalletHub

Note:
Dependency on Federal Government (1 is lowest, 51 is highest)
Tax Rates (1 is lowest, 51 is highest)
Courtesy of WalletHub

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Finally, A Good Social Mobility Study Focusing on Female Mobility

If you follow social mobility studies it won’t come as a surprise to learn the great majority of those studies rely on father-son comparisons, ignoring to a large degree women’s mobility. A much needed intergenerational social mobility study is found in the recent work by Venator and Reeves (2014). Their research compares Baby Boomer women to their parents in terms of wages, hours worked, and where they end up in the income distribution. Six findings are summarized:

1. Today’s working women (henceforth described as “daughters”) have higher wages than their mothers – but do not have higher wages than their fathers. Men have higher wages than both their fathers and their mothers.

2. The poorest women are doing best. 80% of daughters raised in the bottom quintile have higher wages than their fathers did.

3. “Men’s wages remain more important to increasing couples’ family income,” despite “women’s significant generational gains.” Brad Wilcox talks about this finding in greater detail in the Atlantic, arguing that the class gaps in marriage explain why women’s higher individual incomes at the bottom haven’t translated to family incomes at the bottom being higher.

4. Women who grew up in households where their mother did not work actually have the highest family incomes today—but not because they themselves earn more. Daughters’ individual incomes do not vary significantly by mother’s work status, but family income does—suggesting that daughters whose mothers didn’t work have higher earning husbands. (Catherine Rampell discovered this by asking Pew to split out their analyses by mothers’ labor choices.) Perhaps those raised in more traditional settings are more likely to replicate a traditional division of labor?

5. There’s a lot of stickiness at the top and bottom of income distribution—but women suffer more than men at both endsWomen born at the bottom are more likely to remain in the bottom two quintiles than men, but men born at the top are more likely to stay at the top than woman born at the top.

6. More work for women has been good for mobility, particularly for those at the bottom. Pew looked as what would happen to mobility if women’s wages increased as they have over the past forty years, but women’s labor force participation (i.e., hours worked) were the same as in their mothers’ generation. They found that increased work hours resulted in an 11 percentage point increase in upward mobility for women born at the bottom.

Reference

Venator Joanna, and Reeves, Richard V. (2014). Women and Social Mobility: Six Key Facts. Brookings Institution.

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Predicting Low-Birth Weight Outcomes: A Strategy for Intervention

There are some intractable social problems which are extremely difficult to ameliorate but low-birth weight shouldn’t be one of them for a country as rich as ours. It would appear a prudent approach for mediating the adverse effects of low-birth weight outcomes is to know where to look for such outcomes in order to mobilize and deploy sufficient resources to address the problem. In this study I exploit two datasets I merged to understand the association of several county-wide variables with low-birth rates. Before we move to the study’s findings let’s ask why we should be concerned about low-birth weight.

Why is Low-Birth Weight a Major Problem?

The adverse consequences of low-birth weight have been known for a long-time. Low-birth weight outcomes have been correlated with deviant behavior, difficulties in language development, poor academic achievement, lower intelligence, high school dropouts,  hyperkinesis, autism, involvement in childhood accidents, poor cognitive function, slow physical growth, respiratory disease, high blood pressure, diabetes, and heart disease. In addition, women with low income, have low levels of educational attainment, are Black and younger than 17 or older than 35 are more likely to give birth to low-birth weight babies (Bhutta, et al., 2002; Caputo andWallace, 1970;  Hack, et al., 1991; Hack, et al., 1995; Paneth, 1995; March of Dimes, 2014). The first line of defense associated with low-birth weight outcomes should be prevention (Reichman, 2005).

Data, Variables and Hypotheses

The focus of this study is county variations in low-birth weight outcomes in the US. Two datasets were merged: 1) Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, County Health Ranking and, 2) American Community Survey, Demographics – Poverty; Social Determinants of Health – Employment.

With the merged dataset the following variables were available:

Low birth weight variables

The specific research hypotheses are:

  1. The percent low-birth weight outcomes is directly related to the percent of county-wide adults who report no leisure time physical activity.
  2. The percent of low-birth weight outcomes is directly related to the percent of county-wide children who live in single-parent households.
  3. The percent of low-birth weight outcomes is directly related to the percent of county-wide adults who report not getting social/emotional support.
  4. The percent of low-birth weight outcomes is inversely related to county-wide high school graduation rate.
  5. The percent of low-birth weight outcomes is directly related to the county-wide inequality measure, the Gini coefficient. (The Gini coefficent varies between 0, representing complete equality, and 100, representing complete inequality.)
  6. The percent of low-birth weight outcomes is related to regions of the country: South, Northeast, Midwest and West.

Statistical Method

As the dependent variable, low-birth rate, is a continuous variable and the covariates are a mixture of continuous and one categorical variable, the statistical tool of choice is multiple regression.

Findings

The multiple regression produced the following output.

regression output low-birth weight

The full model is statistically significant, Prob > F = 0.0000, as well as all covariates. The model accounts for a large amount, 64%, of the variance in low-birth weights among counties in the United States.

I could spend a few paragraphs interpreting each coefficient with a phrase something like this: With all other variables controlled, a one unit increase in variable x is associated with a y (coefficient) increase/decrease (depending on the sign of the coefficient) in the percent low-birth weight outcomes.

One problem with this approach to interpretation is some of the covariates are measured in different scales. A scale-free method is provided by using elasticity coefficients (Wooldridge, 2002). Below is a table of elasticity coefficients associated with this regression analysis. Elasticity coefficients indicate a proportional change in the dependent variable is associated with a given proportional change in the independent variable.

elasticities - low-birth weight

Controlling for the influence of all other covariates, we can interpret each elasticity coefficient in the following manner:

  1. A 1% increase in the ‘percent of adults whot report no leisure time physical activity’ is associated with a 0.231% increase in babies categorized as low-birth weight. The research hypothesis is supported.
  2. A 1% increase in the ‘percent of children who live in single-parent households’ is associated with a 0.287% increase in low-birth weight babies. The research hypothesis is supported.
  3. A 1% increase in the ‘percent of adults who report not getting social/emotional support’ is associated with a 0.125% increase in low-birth weight outcomes. The research hypothesis is supported.
  4. A 1% increase in ‘Gini coefficient, measuring inequality,’ is associated with a 0.239% increase in low-birth weight babies. The research hypothesis is supported.
  5. A 1% increase in ‘high school graduation rate’ is associated with a 0.145% decrease in low birth weight outcomes. The research hypothesis is supported.

With the regression formula available we can utilize the regression coefficients to predict each county’s expected low-birth weight mean. By sorting the predictions we can identify the top ten counties with the most favorable low-birth weight outcomes and the worst ten counties with the least favorable low-birth weight outcomes.

Ten counties with the most favorable low-birth weight outcomes are:

Lowest low-birth weight counties

Colorado, Utah and Minnesota dominate US counties in producing the best low-birth weight outcomes.

Ten counties with the worst low-birth weight outcomes are:

Ten worst counties with high low-birth babies

Counties in Southern states exhibit some of the worst low-birth weight outcomes. Mississippi has the unenviable distinction of having 6 of the 10 counties with the least favorable low-birth outcomes.

The impact of the Southern region on low-birth outcomes is highlighted in the following  graphs.

Regional Associations with Predicted Low-Birth Weight Outcomes

As the Gini coefficient increases, capturing greater levels of economic inequality, the percent of low-birth outcomes increases in all regions but Southern counties exhibit higher levels of low-birth weight outcomes throughout the Gini coefficient distribution.

Regional Associations with Predicted Low-Birth Weight Outcomes By Percent Single Household

As the percent of children who live in single-parent households increase the percent of low-birth weight babies increase but the regions of the Northeast, Midwest and West exhibit lower predicted low-birth weight means throughout the ‘percent of single household’ distribution.

Regional Associations with Predicted Low-Birth Weight Outcomes By 'Percent No Emotional Support'

An increase in the ‘percent of adults who report not getting social/emotional support’ is associated with an increase in low-birth weight outcomes but again Southern counties exhibit higher means throughout the ‘no social/emotional support’ distribution.

Regional Associations with Predicted Low-Birth Weight Outcomes By Percent Physically Inactive

Low-birth weight outcomes are directly related to the ‘percent of county-wide adults who report no leisure time physical activity’ in all regions of the US but Southern counties have higher predicted low-birth means than the other US regions.

Regional Associations with Predicted Low-Birth Weight Outcomes By High School Graduation Rate

As hypothesized there is an inverse relationship between low-birth weight outcomes and high school graduation rates. While this relationship holds for all US regions we nevertheless observe Southern counties with higher, unenviable low-birth predicted means throughout the graduation rate distribution.

The observant reader will have noticed that the predicted low-birth weight means appear to be very close in the above graphs for the West, Midwest and Northeast regions.  There are differences in the predicted means but are they significantly different?

Let’s begin by using Stata’s ‘contrast’ command to test the overall effect of the factor variable, region_cat.

Contrasting regions against the reference group - South

The above table demonstrates the overall effect of region_cat is significant (P>F = 0.000). We can say that region_cat deserves to be in the model.

What about the contrasts between the other regions? To answer this question we use Stata’s ‘pwcompare’ command, producing the following table.

Pairwise regional contrasts - low birth weights

The first three contrasts duplicate the results we just interpreted. However, the last three contrasts reveal non-significant differences in low-birth weight means between West, Midwest and Northeast regions. The only significant mean difference contrasts are those which involve the South. The other contrasts are not significantly different at an conventionally used level of significance.

Conclusion and Policy Implications

This analysis has demonstrated low-birth weight outcomes can successfully be predicted by county-wide variables. If we can successfully predict where low-birth weight outcomes are more likely to occur it then becomes prudent to mobilize and deploy resources in those areas where known medical and social interventions can mediate the adverse effects of low-birth weight.

Reference

Bhutta, Adnan T., et al. (2002). “Cognitive and behavioral outcomes of school-aged children who were born preterm: A meta-analysis.” Journal of the American Medical Association. Volume 288, Number 6: 728–37.

Caputo, Daniel V.; Mandell, Wallace. (1970). “Consequence of low birth weight.” Developmental Psychology, Vol 3(3, Pt.1), 363-383. doi: 10.1037/h0030030

Hack,Maureen; et al. (1995). “Long-term developmental outcomes of low birth weight infants.” The Future of Children. Vol. 5, No. 1, pp. 176-196.

Hack, Maureen; et al.  (1991). “Effect of very low birth weight and subnormal head size on cognitive abilities at school age.” New England Journal of Medicine. 325:231-237/ doi: 10.1056/NEJM199107253250403

March of Dimes. (2014). Working Together for Stronger, Healthier Babies.

Paneth, Nigel. (1995). “The problem of low birth weight.”  The Future of Children. Vol 5, No. 1: 19–34.

Reichman, Nancy E. (2005). “Low birth weight and school readiness.” The Future of Children. Volume 15 Number 1.

Wooldridge J. M.  (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.

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