As the response varied significantly between subjects in terms of which electrode showed the largest effect, we quantified the data using the same procedure reported by Gratton et al. Note that ERP data were not useable for two subjects 1 younger and one older high-fit subject. Only a subset of the source laser-diodes were used at one time, using the montage described in Figure 1.
The light sources were 0. Source and detector fibers were held in place using modified motorcycle helmets. Hair was combed away from under the detector fibers before they were put into place; the source fibers, being much smaller, could be placed through the hair. Source fibers were paired, so that each location was illuminated by two fibers, one connected to a nm laser and the other to an nm laser; the two fibers were never on at the same time.
The light sources were time-multiplexed in cycles of 16 ms divided into ten 1. Positions of the sources and detectors used in the study. Large circles represent detectors and small circles sources. Red and blue colors represent the two recording montages. The graph shows a schematic back view of the recording helmet. The approximate position of the inion is marked by a green cross.
The light sources were modulated in intensity at MHz. Because of the low absorption and high scattering of tissue at the wavelengths used and nm , the resulting irradiation is several orders of magnitude below OSHA safety standards. The detector amplifiers were modulated at a frequency of This created a cross-correlation or heterodyning or beating frequency of 6.
This frequency was used for the recording of frequency-domain data. The optical data from each detector were sampled continuously at a frequency of 50 kHz. Eight points were collected for each oscillation of the cross-correlation frequency. To eliminate the possibility of cross-talk between sources, the first two cycles. The remaining eight oscillations for a total of 64 digitized points were used to compute a fast Fourier transform FFT yielding estimates of DC intensity zero-frequency or average effects , AC intensity amplitude of oscillations at the 6.
This procedure comprised the following steps. Third, for each participant the MR and optical source and detector points were Talairach-transformed to place them in a common space. Fourth, to further reduce the influence of anatomical variability across participants, the individual anatomical images were centered on the midpoint between the most posterior points of the left and right calcarine fissures as identified in the structural MR images.
This allowed for a more accurate between-subjects co-registration of visual cortex. Fifth, surface projections of optical effects at various latencies from stimulation were obtained using a 2. Slow drifts in the phase data were then corrected using a polynomial regression method effectively eliminating frequencies below 0. A pulse correction algorithm developed by Gratton and Corballis was then applied.
DC and AC intensities were normalized by dividing them by the average value across each block. Following our previous work, for the analysis of EROS we employed the phase data for both nm and nm channels. For each channel i. Although we accepted channels with SDs up to ps i. The data were then segmented into ms epochs, time-locked to each checkerboard reversal i. The first five stimulations from each block were discarded to generate stable stimulation conditions. The remaining epochs were used to compute average waveforms for each measurement type DC intensity, AC intensity, and phase delay 2.
The data were then filtered to eliminate frequencies above 10 Hz, and a baseline estimated as the average of a ms peri-stimulus period was subtracted from the data. This baseline was selected to reduce the impact of the previous trial while maintaining sufficient baseline duration. Note that this impact was less severe than for the ERP data because the greater spatial resolution of EROS results in a smaller overlap between the brain responses elicited by consecutive stimulations.
For phase data, channels are averaged a product would not make senses since some of the values are positive and some are negative. For each participant a 2. For each location of the grid, the channels whose recording volumes encompassed the location were then determined independently for each participant.
For DC intensity, AC intensity, and modulation, the logarithms of the values were averaged.
For phase data, the original value rather than the logarithm was used because only relative values are obtained. For phase data, the different channels were then averaged together. We did not weight the channels differently depending on their intensity because this procedure would give more weight to channels with short source-detector distances, which are unlikely to probe the brain.
Most points in the central grid region were within the path of several up to 20 channels. For each measurement type, the previous methods yielded a grid map of activity for each time point 15 time points; sampling rate: 16 ms , stimulation condition, and participant. These maps were spatially filtered using an 8-mm Gaussian filter 2 cm kernel , and used for statistical analysis. This analysis was conducted by computing t -score maps related to the difference between each value and the baseline.
Each of these t -score maps was then converted into Z -score maps. For statistical analysis and quantification we used a ROI-approach to reduce multiple comparisons as is typically done for neuroimaging data. Only the data within this ROI were included in the subsequent statistical analyses and quantification. First, we examined whether a significant response was obtained in area 17 at the predicted latency of activation 96 ms , when averaged across subjects and conditions, both with respect to the baseline level and with respect to the short-distance channel control condition.
Following the methodology commonly used to correct for the problem of multiple comparisons, the significance of a response was estimated by comparing the Z -score value observed at the peak point with a criterion value estimated using the random-field theory see Friston et al. Then, for each subject and condition we quantified the amplitude of the neural response in BA17 by averaging the time course of the EROS response for all voxels within the preset ROI, and selecting the point in the waveform with the largest response amplitude in an interval between 64 and ms from stimulation.
These peak values were used for the comparisons across stimulation conditions and for the neurovascular coupling analysis. Only one value per condition per subject was entered in the analyses. Thus, averaging across optode pairs and frequency and spatial filtering which were carried out separately for each subject and condition do not lead to correlated error terms for the statistical analyses and hypothesis testing, since the error terms and degrees of freedom were always computed across subjects apart from the lack-of-sphericity issue described below.
Data across subjects were analyzed using t -tests transformed into Z scores or using repeated-measures ANOVAs when appropriate, with age as a between-subject factor and frequency of stimulation as a within-subject factor. The major differences were as follows. First, AC intensity data were used instead of phase data.
This is because the formulae used to derive the concentration of [HbO 2 ] and [HbR] have been developed for intensity but not for phase data Boas et al. Second, the data were averaged across blocks rather than across trials separately for each subject, condition and channel, using a baseline determined by the last 10 seconds before stimulation note that, as the data were divided by the baseline value, they hovered around 1. Third, for each grid location within the same grid used for the analysis of EROS , the data were transformed into [HbO 2 ] and [HbR] changes using the formulae described by Boas and coll.
This yielded average estimates across blocks of the time courses of [HbO 2 ] and [HbR] changes for each location, starting 10 seconds before the beginning of the stimulation period, and lasting throughout the These waveforms were down-sampled at one value every 1. Fourth, the average change from baseline during the period between 5 and Of these, only the C1 component, which has a peak latency between 80 and ms, is supposed to be generated in area 17 Martinez et al.
The amplitude of the C1 component was quantified as the maximum standard deviation across electrodes for each frequency between 64 and 92 ms for the younger adults and between 84 and ms for the older adults see Gratton et al. The amplitude of C1, reported in Figure 3A , varied with stimulation frequency and age. Specifically, C1 amplitude decreased as stimulation frequency increased, F 3.
This variation in amplitude of the C1 as a function of age may reflect small but consistent anatomical variations in the shape of BA17 in different age groups, perhaps as a consequence of shrinking or displacement of the occipital cortex in the older relative to the younger adults as reported by Salat et al , which may direct the dipole towards the surface in the older group. A Time courses of the ERP response at the Pz electrode averaged across subjects separately for each age group and stimulation frequency conditions.
The error bars represent standard errors of the mean computed across subjects. Figure 4A shows grand average maps of the EROS response at a latency of 96 ms, collapsed across all stimulation frequencies back view of the brain. These data are plotted so that only voxels with Z scores greater than 2. These maps indicate the presence of robust and consistent responses within the BA17 ROI, which were absent when short source-detector distances were examined middle panel. A: Z-score maps of the EROS data at a latency of 96 ms after stimulation, averaged across all subjects and stimulation conditions.
The surface projection of BA17 is outlined in green on each of the maps. Left : Average map across all subjects for source-detector distances between 20 and 50 mm. Middle : Average map across all subjects for source-detector distances between 0 and Right : Difference between the other two maps.
For the short-distance channels, the peak Z value was equal to 1. This figure indicates that the peak of EROS activation in BA17 occurred at 80 ms in the young subjects and 96 ms in the old subjects, very close to the predicted values, and to the latencies of the C1 response in ERPs presented in figure 2A. Additional data are also included in Figure 2 for reference purposes. We chose this analytic approach average of the ROI because we expect it to be more stable and less likely to capitalize on chance.
As mentioned, the amplitude of the EROS response was quantified as the maximum value for the average across the entire BA17 ROI in the interval between 64 and ms for each subject and frequency condition. Only waveforms with an identifiable peak were used for this analysis see Methods section. The mean values across subjects for the young and old groups are presented in Figure 3B. As for the C1 ERP component, these data show a response that decreases as a function of frequency of stimulation F 2.
This discrepancy probably reflects the fact that, differently from the ERP, EROS is not influenced by the orientation of dipoles with respect to the surface of the head. Figure 5 and Figure 6 report Z -score maps across all subjects of the change in [HbO 2 ] Figure 5 and [HbR] Figure 6 during the interval between 5 and The data were averaged across all stimulation frequencies.
In the top rows are the data for all subjects together, both for long- and short-distance channels, as well as their difference. In the middle row are separate maps for young and old subjects. The maps indicate a diffuse hemodynamic response encompassing several areas and spreading much further than the EROS response.
This probably reflects the fact that different regions of the visual cortex are activated at different latencies from stimulation, and therefore are visualized together by the hemodynamic maps which reflect slow phenomena but not by the EROS maps which reflect the activation at 96 ms latency only. No clear response was visible in the short-distance control channels.
Separate maps for the different age groups, however, revealed a clear activation in the younger adults, but no significant activation in the older adults. These data suggest that the amplitude of the [HbO 2 ] response is influenced by both age and fitness level. A: Z-score maps of the [HbO 2 ] values between 5 and 15 seconds after the onset of stimulation.
B : Average Z-score maps of the [HbO 2 ] data for young left and older middle adults. C : Average time course across all voxels of BA17 for young and older subjects. A: Z-score maps of the [HbR] values between 5 and 15 seconds after the onset of stimulation. B : Average Z-score maps of the [HbR] data for young left and older middle adults. We expected that neural activity would induce a reduction in [HbR], as a function of the increase in blood flow.
The results presented in Figure 6 show a widespread reduction in [HbR] as a function of stimulation. However, the correlation between [HbR] change and VO 2max in the older adults was not significant, suggesting that the age difference was probably not due to changes in physical fitness, and generally dissociating the [HbO 2 ] and [HbR] responses, with one showing significant effects of age and the other showing significant effects of fitness.
A similar variation in the coupling was observed when analyzing the correlations between [HbO 2 ] and [HbR] changes in individual subjects across stimulation frequency conditions the statistical tests of these correlations were based on their Fisher transforms. Thus the data indicate an age-related variability in coupling of different components of the neurovascular response see also Safonova et al. This is also consistent with the idea that, across subjects differing in age and level of fitness, the coupling between deoxy- and oxy-hemoglobin, as well as between oxy-hemoglobin and neuronal activity, are somewhat degraded.
The relationship between these responses and stimulation frequency was clearly not linear. The lack of linearity is not surprising, as the neuronal measures both EROS and ERPs showed a marked reduction of the amplitude of the response as a function of stimulation. As such, we should not expect the amplitude of the hemodynamic response which accrues over an extended period of time to linearly increase with the stimulation frequency.
In fact, the actual increase of the hemodynamic response as a function of stimulation frequency was overall quite modest. Changes in hemodynamic parameters in BA17 during the stimulation period with respect to baseline, as a function of stimulation frequency and age group. The vertical bars indicate standard errors of the means computed across subjects. As a reminder, to evaluate the quantitative relationship between neuronal and hemodynamic measures as a function of age and fitness, we compared the way in which the two sets of measures respond to visual stimulation frequency.
Hemodynamic measures relate to phenomena that accumulate over time. Hence, the effect of each individual stimulus on the hemodynamic response can be estimated by dividing the actual response by the stimulation frequency. If the relationship between neuronal and vascular responses is linear, these derived measures should correlate linearly with the fast measures integrated over time. An alternative model assumes that this relationship is not linear, but likely saturates at high levels of neuronal activity.
In this case we might assume that a decelerating function, such as the square root of the integrated fast signal, would be a better predictor of the hemodynamic response. Since the quadratic model does not include a linear predictor, both models have the same number of free parameters, and therefore they are directly comparable to each other. We computed the integrated fast response EROS and ERPs over time for each stimulation frequency condition by multiplying the fast response by the stimulation frequency and, for each subject separately, correlated it with the amplitude of the changes in hemodynamic measures for the same conditions.
In the linear model, we used the actual values of the integrated fast response as predictors. In the quadratic model, we used the square roots of these values. Note that this model was chosen as an example of decelerated function, indicating saturation of the hemodynamic response at high levels of neural activation. A logarithmic or exponential model would likely produce very similar results, but these models cannot be effectively separated from the quadratic model tested here with the small number of available points per subject.
As we assume that the absence of stimulation would not generate a response, we added an additional point to the computation of the correlation coefficients in which both the neuronal and the hemodynamic responses are equal to 0. For all statistical analyses of the correlation coefficients across subjects we used their Fisher transforms. The average Fisher-transformed data were then reversed to correlation coefficients for display purposes. Scatter plots between the average predicted values according to the linear and quadratic models and the various neurovascular effects for each stimulation frequency condition are shown in Figure 8.
The correlations between the predicted and actual neurovascular responses for both the linear and the quadratic models, computed separately for each subject and then averaged across subjects, are presented in Figure 9. Scatter plots depicting the relationships between the predicted and observed hemodynamic responses for each stimulation condition averaged across subjects. Average correlations between the observed hemodynamic response and that predicted on the basis of the integration of the fast EROS response over time for younger and older adults.
The vertical bars indicate standard errors of the mean computed across subjects. Essentially identical results were obtained when using the ERP measures to estimate the neuronal response. These data indicate that the hemodynamic response is better correlated with the square root of the integrated neuronal response than with the untransformed value. This suggests that, at least in visual cortex, the neurovascular relationship departs markedly from linearity and tends to generate a decelerated function with reduced increases in hemodynamic responses as a function of additional equal increases in the neuronal response.
In addition, the coupling between oxy- and deoxy-hemoglobin response decreases with aging at least in occipital regions, and there is some hint to a break-down of the neurovascular coupling in general in older adults, although this appears to depend on the hemodynamic variable used for the estimates. In this respect, the data differ from previous reports obtained with optical methods Gratton et al.
Older adults also show, in general, a similar relationship to younger adults between neuronal and vascular measures when the BOLD fMRI and [HbR] responses are considered, although the amplitude of the component responses both neuronal and hemodynamic is somewhat depressed relative to that of the younger subjects. This confirms previous findings for visual areas e. However, for older adults a different picture emerges when [HbO 2 ] changes are taken into consideration. In this case, the neurovascular coupling is somewhat disrupted in the low-fit older adults, as demonstrated by the very reduced [HbO 2 ] response in this group — even though the neuronal response is equally large in the high-and low-fit subjects.
These age-related changes in neurovascular coupling are likely to reflect problems within the vascular system, which may emerge in aging. In fact, these changes appear particularly evident in subjects with low cardiorespiratory fitness. It should be noted that the older sample in our study was screened for a number of possible health problems. Specifically, no severe hypertension cases were included in our sample, and subjects were in sufficient good health to be able to complete the maximal graded exercise test.
Nevertheless, the low-fit subjects in our study had, on average, higher blood pressure and higher body-mass index BMI. Thus, it is likely that the reactivity of the vascular system in low-fit older adults was somewhat poorer than in those older adults with higher fitness levels and in the younger adults. A possible explanation for this phenomenon is that the vascular system of low-fit older adults may have lost some of its ability to adapt to increased task demands, with blood vessels already being dilated even during the rest conditions.
Another possibility which is not inconsistent with the previous account is that low-fit older adults may have lost some of their brain capillary bed. This is consistent with the notion that aerobic exercise which increases cardiorespiratory fitness leads to an increase in angiogenesis Isaacs et al. The data presented here have important implications for functional imaging studies and for the interpretation of functional aging data.
First, at least for visual cortex, they indicate that some caution should be taken when using linear decomposition methods, such as SPM or fast event-related fMRI, in the analysis of hemodynamic imaging data, in particular from older adults, because the neurovascular coupling function shows signs of saturation at higher levels of neuronal activation. This non-linearity may be mediated in part by the different spatial extent of the neuronal and hemodynamic responses.
In any case, these data indicate that caution should be taken when inferring neuronal activity from hemodynamic signals. Second, this is likely to be even more problematic in low-fit older adults, who show a depressed neurovascular response at least for measures of [HbO 2 ]. Third, the data indicate that fitness level is important for neuroimaging studies of aging. Specifically, they show that age-related reductions in hemodynamic brain responses at least for [HbO 2 ] measures may not reflect similar reductions in neuronal responses.
Several previous studies have compared hemodynamic responses in older adults with those of younger adults. However, some of these studies involved only hemodynamic measures. In this case, it is difficult to determine whether the observed differences had to be attributed to changes in neuronal function or changes in neurovascular coupling. Further, for the most part, the effects reported in these studies were quite variable across individuals, perhaps because fitness was not controlled for, and subjects could vary significantly with respect to this variable.
The current experiment confirms the presence of a depression of neurovascular coupling in low-fit older adults and indicates that fitness may be an important source of individual differences, especially among the elderly. The data indicate variability in the coupling of the [HbO] and [HbR] responses to stimulation as a function of aging. This finding is in line with that reported by Safonova et al. Specifically, older adults showed a low correlation between the two responses.
This may in part be due to the fact that both responses were smaller in older than in younger adults, and therefore more difficult to measure on individual subjects. However, figure 7C also indicates that the compensatory vasodilation indexed by the BOLD response was reduced in older adults. In other words, the increase in blood flow may have been too small to lead to an increase in [HbO] in at least some of the older adults, as all the incoming oxygen may have been used up by the activated tissue.
This study also highlights the potential utility of optical imaging as a tool for studying neurovascular coupling. An advantage of optical imaging is that measurement of neuronal and hemodynamic parameters could be taken simultaneously. A further advantage is that these measures can be referred to specific brain regions, and that, in principle, given the appropriate paradigm and recording methods more than one region could be studied in parallel.
This could be used to evaluate whether neurovascular coupling changes significantly with location within or across individuals or groups. Although our study focused on optical measures, we also recorded ERP activity. The two modalities responded similarly to the frequency manipulation. They also showed a good correspondence in terms of latency of the response, and between the patterns of activities at different latencies in different age groups.
Two factors may account for this finding. First, the EROS response is more precisely localized than the ERP response, and therefore less susceptible to cross-talk from different brain regions. Second, just like the hemodynamic measures BOLD fMRI and NIRS signals the EROS response is related to scalar properties scattering changes of the activated area, whereas the ERP response is related to vectorial properties of the brain, and specifically to the orientation in space of the cortical region involved in its generation.
If the orientation differs systematically across subject populations, the relationship between ERPs and the hemodynamic measures may also vary. As a consequence, the slopes of the neurovascular function for different subject groups may not align. The current study also had several limitations, which should lead to further investigation. First, only visual stimulation and occipital cortex were investigated.
Further studies will need to be carried out to determine whether the finding of a non-linear relationship between neuronal and hemodynamic measures is generalizable to other cortical regions. Indeed, some investigators suggest that the relationship between neuronal and vascular measures might vary in different cortical regions e. Second, fitness was evaluated across individuals, in a correlational rather than experimental fashion.
Recent work shows that aerobic exercise may result in changes in several functional parameters Emery et al. It is likely that it may also lead to changes in neurovascular coupling. It is however also possible that fitness per se may not be the critical variable, but only a correlate of some other factors e. These variables may be studied independently and in combination to determine their relative influence on neurovascular coupling.
Third, it is not clear from the current data whether the reduction in neurovascular coupling observed in the low-fit older adults has any functional counterpart. This is because the task used in the current study passive viewing of a reversing checkerboard did not result in any behavioral measure. To evaluate the functional impact of reduced neurovascular coupling it will be necessary to employ more informative behavioral manipulations, perhaps including tasks varying in difficulty.
Fourth, we only used one way of manipulating the amplitude of the hemodynamic response in the current study i. It is possible that different rules would regulate neurovascular coupling when other manipulations are used, such as the duration of the stimulation or its intensity. This remains a subject for future investigation. In conclusion, this study presents an investigation of neurovascular coupling in younger and older adults differing in fitness levels, based on optical imaging as well as electrophysiological and fMRI measures. The results indicate the presence of a non-linear relationship between neuronal and hemodynamic effects in younger adults and in older adults with higher fitness levels, and an alteration of the hemodynamic response in older adults with lower fitness levels.
The study also illustrates an approach to the investigation of neurovascular coupling based on the use of hemodynamic and neuronal optical measures, which in the future could be extended to other cortical regions and help inform the interpretation of cognitive neuroscience data. We wish to thank Yukyung Lee, Katherine S. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript.
The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Phase delay is a circular periodic variable. Therefore the average of values that are at the two sides of the wrapping point degrees is misleading. For example, the average of and 1 should be or 0, and not However, when the variability of the phase is small less than 30 degrees the probability of two points of the distribution to be on opposite sides of the wrapping point is negligible, unless the mean value is already close to or 0.
In our data we eliminated this last possibility by applying a phase wrapping correction before averaging.
Papers discussed in this category
Recent Activity. However, the coupling between oxy- and deoxy-hemoglobin changes decreased with age and increased with increasing fitness. The snippet could not be located in the article text. This may be because the snippet appears in a figure legend, contains special characters or spans different sections of the article. Author manuscript; available in PMC Jan PMID: Monica Fabiani , Brian A. Gordon , Edward L. Maclin , Melanie A. Pearson , Carrie R. Brumback-Peltz , Kathy A. Sutton , Arthur F. Kramer , and Gabriele Gratton. University of Illinois at Urbana-Champaign.
Address all correspondence to: Prof. Mathews Ave. Copyright notice. The publisher's final edited version of this article is available at Neuroimage. See other articles in PMC that cite the published article. Abstract Brain aging is characterized by changes in both hemodynamic and neuronal responses, which may be influenced by the cardiorespiratory fitness of the individual. Introduction The most commonly used methods for functional neuroimaging, such as functional Magnetic Resonance Imaging fMRI and in particular the blood-oxygen-level dependent BOLD response, are based on imaging some of the hemodynamic consequences of neuronal activity.
Methods Participants Nineteen young adults 9 females, ages 20—28 and forty-four older adults 24 females, ages 65—81 participated in a five-session experiment. Table 1 Means SDs for the demographic and neuropsychological variables characterizing the subject groups. O t test: OH vs. OL Number of Females 9 11 13 n. Age years VO2max - Open in a separate window. OH vs. Biometric variables For the older adults only, physical fitness was evaluated using the VO 2max index, using a modified Balke protocol American College of Sports Medicine, Table 2 Means SDs for the physiological variables characterizing the subject groups.
OL Height cm - Weight kg - Heart Rate per min Sessions The data presented in this study were collected during five sessions. Figure 1. Co-registration of optical and MR anatomical data This procedure comprised the following steps. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Gornick, J. Hausman, J. Sources of bias and solutions to bias in the Consumer Price Index.
Hurst, E. Interagency Technical Working Group. Johnson, N. Inflation Calculation and their Implications. Martin, J. Births: Final Data for Meyer, B. Measuring the well-being of the poor using income and consumption. Identifying the disadvantaged: Official poverty, consumption poverty, and the new supplemental poverty measure. Annual Report on U. Consumption Poverty: Household surveys in crisis. Moffitt, R. Trends in the Level and Distribution of Income. National Research Council. Measuring Poverty: A New Approach. Norris, T. Pinkovskiy, M. Shining a Light on Purchasing Power Parities.
Rainwater, L. Renwick, T. Median Rents from the American Community Survey. Schaefer, A. Short, K. Experimental Poverty Measures: Government Printing Office. Taking Account of Taxes and Transfers in Tax Foundation. Tax Policy Center. EITC Parameters, to Supplemental Nutrition Assistance Program, July Fertility: American Community Survey 1-year Estimates.
Urban Institute. Wheaton, L. Washington, DC: Urban Institute. Wimer, C. Cantillon, S. Handa, and B. Nolan Eds. Oxford: Oxford University Press. Columbia University Academic Commons. Progress on poverty? New estimates of historical trends using an anchored Supplemental Poverty Measure. Winship, S. Poverty After Welfare Reform. New York: Manhattan Institute. In Chapter 3, the committee elected to focus on studies that estimated a causal relationship between poverty and child outcomes.
In this appendix, we try to give a broader overview of some of the correlational lit- erature, in order to show the pervasiveness of the relationship across many different types of outcomes. This appendix section is not intended as an exhaustive review of the literature. We also touch upon how these factors can contribute to the intergenerational transmission of poverty. In addition to the narrative overview, we also offer summary tables of the material treated in the text, one for each section.
These tables enable readers to quickly scan the findings, or to locate particular studies. This appendix ends with a consideration of the literature about the relationships between child outcomes and the timing and severity of poverty. The litera- ture is also summarized in companion tables. ACEs include abuse or neglect, the death of a parent, divorce or separation of parents, domestic violence, neighborhood violence, family mental illness or sub- stance abuse, and incarceration of a family member.
Poor and near-poor children are more than twice as likely to have experienced three or more ACEs than their more affluent peers Anda et al. Expe- riencing ACEs early in life has been shown to be predictive of long-lasting negative outcomes in adulthood, such as increased risk for cardiovascular disease, obesity, smoking, drug and alcohol abuse, risky sexual behavior, and early mortality Anda et al.
It is thought that these life events cause high levels of biological stress on the developing brain, as well as on neurological, hormonal, and immune-response systems, leading to lifelong changes Shonkoff et al. On average, infants born to mothers hospitalized for assault weigh grams less at birth. Since poverty is now one of these ACE questions asked of parents, analysis by poverty levels excludes the experience of economic hardship.
We explore two ACEs, child maltreatment and intimate partner violence, below. Researchers have repeatedly documented the association between pov- erty and the risk of child maltreatment, especially in cross-sectional studies of the general population. Children in low socioeconomic status SES fami- lies were five times more likely than those in higher income families to expe- rience maltreatment of any kind, three times more likely to be physically or sexually abused, and seven times more likely to be neglected Sedlak et al.
In a study looking at the impact of welfare reform, higher poverty rates were associated with significantly more substantiated cases of child. Note: Based on the U. Resources and Services placed in out-of-home foster care come from homes that are eligible for welfare Macomber, Analyzed by the Health Resources and in Wisconsin to distinguish the causal effect of income from other causes of ofchild Maternal and Child Health Bureau.
Mothers received tional income, on average,for Health Statistics, National per year for Health. Low-income families also experience intimate partner violence at higher rates than other families. Furthermore, the rate of hospitalizations for domes- tic assault while pregnant is three times as high for mothers who are on. These women are more likely to have poor birth outcomes such as low birth weight infants, fetal death, and increased infant mortality Aizer, On aver- age, infants born to mothers hospitalized for assault weigh grams less at birth Aizer, The lower birth weights of these infants are likely to lead to poorer heath, lower academic achievement, and reduced income in adulthood, thereby contributing to intergenerational poverty Almond and Currie, The causal relationships between poverty, child maltreatment, and intimate partner violence have been less clear.
It is unknown if the higher rates of child maltreatment and domestic violence in low-income families reflect the increased stress of material and economic hardship, preexisting conditions such as mental illness and substance abuse , the increased contact with mandatory reporters of child maltreatment, or attribution bias among mandatory reporters. Alternatively, conditions that can lead to poverty and to child maltreatment, such as parental mental health problems and substance abuse, are causing correlations between poverty and child maltreatment that are not causal.
Children in low-income families are more likely to have a parent with poor mental health compared with children in higher income families Macomber, As is often the case with a physical health problem, mental health problems can detract from the care and attention that a parent can provide their child. And finally, it is possible that the disparities in rates of child maltreatment and intimate partner violence between poor and higher-income families are a product of reporting bias.
Poor families are more likely to come into contact with mandated reporters of child maltreatment through their participation in government systems, such as welfare. This increased visibility may lead to greater attention to problems in these families than in families with higher incomes, who are less likely to use welfare and other assistance programs.
Many, but not all, of the negative impacts of poverty on child outcomes may be mediated through those material hardships. Measures of material hardship use indicators of con- sumption and physical living conditions that are directly related to whether families can meet basic needs, starting with needs for physiological func- tioning and survival Ouellette et al. Therefore, measures of shelter. APPENDIX D housing, utilities , medical care, food security, and ability to pay for essen- tial expenses are always included when researchers measure material hard- ship.
Sometimes durable goods such as refrigerators, and neighborhood characteristics are also included Ouellette et al. Census Bureau in , included questions on material hardships and provided researchers the ability to look at the relationship of level of income poverty, as well as other characteristics of poverty such depth and persistence with various categories of material hardship. Appendix Figure Number of material hardships by family federal poverty level, Source: Mayer and Jencks, Poverty and the Distribution of Material Hardship.
Gershoff et al. Hardships are also likely to coexist for poor families, with 71 percent of families below 50 percent of the federal poverty level FPL and 54 percent of families between 50 percent and 99 percent of FPL having two or more material hardships, and 38 percent of those below 50 percent of FPL and For example, Gershoff and colleagues used a nationally representative sample of children developed by the U. They also examined the mediating effects of material hardship as well as those of parenting stress, positive parenting, and parental investment in their children.
All of these mediators, including material hardship, were partial, meaning that there were still direct effects of poverty on child out- comes. However, material hardship was highly correlated with parenting stress, which negatively impacted parenting behaviors leading to worse outcomes in child social emotional competence. In fact, material hardship explained most of the impact of family income on social-emotional out- comes. Family income had its strongest direct effects on parent investment in their children, which in turn impacted child cognitive skills very strongly Gershoff et al.
They are more likely to be born at a low birth weight and to die during their first year of life; to experience an injury or poisoning requiring medical attention; to have elevated levels of lead in their blood; to experience a chronic disease such as asthma and obesity; and to experience food insecurity Brooks-Gunn and Duncan, ; Chaudry and Wimer, ; Moore et al. In adulthood, the direction of this gradient is difficult to determine. Does poor health lead to low income or does low income lead to poor health? In children, the direction of causality is clearer since children do not contribute to family income in the United States.
Parental income buffers children from the impacts of chronic diseases, and for almost every chronic condition, children from wealthier families experience better health Case, Lubotsky, and Paxson, For example, poor children with asthma in the United States were almost 12 percent more likely to be in poor health 5 years later, whereas children with asthma from a higher-income family were only 4 percent more likely to be in poor health in that same time period Condliffe and Link, Studies from peer English-speaking nations with universal health care have documented a similar relationship between family income and child health, although the magnitude of the relationship is not as pronounced.
For example, Currie and Stabile , using a panel of Canadian chil- dren, found a flatter gradient between income and child health, although it also steepens as children get older. The data indicate that poor children are subject to more health shocks due to chronic diseases as they get older, which explains the steepening gradient with child age in Canada Currie and Stabile, A study of Australian children also confirmed the exis- tence of an income-child health gradient that is flatter than the U. These studies from countries with universal health care indicate that availability of health insurance is not suf- ficient to eliminate the income gradient in child health, although it appears to reduce it.
A more general look at the relationship between income and child health is provided in Institute of Medicine , pp. Poorer children, due to the income-child health gradient, enter adulthood with poorer health and lower educational attain- ment, likely leading to lower adult earnings Case, Lubotsky, and Paxson, We know that there are large inequalities in infant health at birth which can be crudely measured by the incidence of low birth weight and that these inequalities are associated with socioeconomic factors such as race, maternal education, marital status and income.
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Defining maternal disad- vantage in this way, the incidence of low birth weight is more than three times higher among disadvantaged mothers compared with highly advan- taged mothers Aizer and Currie, Maternal health behaviors during pregnancy can impact birth out- comes, and health behaviors during pregnancy are better among mothers with higher incomes. Sibling studies where the mother smoked during one pregnancy and not the other show similar impact on low birth weight Aizer and Currie, Harmful environmental factors also impact infant health at birth.
They are also less likely to be able d to move to cleaner areas Currie, ; Currie et al. Natural exper- iments that change the level of pollution due to policy changes have been shown to reduce the incidence of low birth weight by more than 10 percent Currie and Walker, Maternal health and nutritional status also impact fetal and infant health.
Poor women are more likely to have preexisting obesity, diabetes, hypertension, and asthma when they become pregnant Aizer and Cur- rie, They are also more likely to be exposed and to be susceptible. Reductions in smoking during pregnancy are associated with higher birth weights and less prematurity.
Using data from flu epidemics researchers have shown that influenza during pregnancy has negative effects on infant birth weights, primarily for mothers who have other indicators of poor health. Disadvantaged mothers are also about one-half as likely to gain the recommended weight during pregnancy compared with more advantaged mothers and nutritional interventions during pregnancy have been shown to increase infant birth weight Ludwig and Currie, In summary, there are many reasons that poor women are at increased risk for infants born with lower birth weight, prematurity, and poorer health.
Maternal behaviors such as smoking, increased exposure to pol- lution, and violence, and worse maternal health and nutrition all have negative impacts on infant health at birth. It is possible that these outcomes reflect structural brain changes in poor children. Children in poor families have recently been documented to have reduced volumes in the cerebral cortex and hippocampus in areas that are key for school readiness and academic achievement National Scientific Council on the Developing Child, These areas are associated with executive function, language development, and memory.
Other studies have shown that parental income is related to the surface area of the frontal, temporal, and parietal cortex of children Noble et al, Poverty causes increased exposure to ACEs and material hardship, as described above, which may induce toxic stress. Toxic stress activates the hormonal systems that respond to stress, especially the hypothalamic-pituitary-adrenocortical HPA system, causing sustained high levels of cortisol and corticotropin-releasing hormone, as well as of inflammatory cytokines Shonkoff et al.
Elevated exposure to stress appears to modify the physiologic response to stress in ways that alter neuroendocrine activity and neural activity, and ultimately brain development and function, in ways that adversely affect the regulation of emotion and attention Blair and Raver, Poor children. One biomarker for chronic stress is telomere length. Theall et al. Exposure to chronic stressors is associated with more externalizing problems and a higher level of behavior problems in children e.
There is increasing evidence that toxic stress in the prenatal period and early childhood can influence long-term child outcomes by chemically alter- ing the structure and function of genes. They may lead to impairments in learning abilities, and increased risk of mental illness, asthma, hypertension, heart disease and diabetes as adults.
Not all epigenetic changes are harm- ful. Research has also shown that positive epigenetic changes in brain cells occur as cognition and memory develop, and repeated stimulation of these brain circuits through positive interactions with the environment and sup- portive adults e.
Neurovascular coupling in normal aging: A combined optical, ERP and fMRI study
Telomeres are DNA-protein complexes at the end of chromosomes. Telomeres shorten with age, and toxic stress is believed to accelerate shortening Mitchell et al. A study of 9-year-old African American boys compared telo- mere length in children raised in disadvantaged environments and those in advantaged environments.
In addition, severe psychosocial stress in pregnancy has been shown to be associated with shorter telomeres in young adult offspring, and young children exposed to violence have increased telomere shortening when tested at age 10 compared with telomere length when they were 5 Price et al. A study of children growing up in poor neighborhoods with a high concentration of poverty, unemployment, and physical disorder docu- mented the association of neighborhood level factors with reduced telomere length as well Theall et al. Taken together, these studies provide evidence that chronic stress associated with socioeconomic disadvantage leads to accelerated telomere shortening, especially when experienced early in life.
Since telomere length is a proxy for cellular aging, and is associated with diseases like diabetes, cancer, and heart disease, and with psychiatric conditions such as depression, this accelerated telomere shortening associ- ated with poverty puts these children at risk for serious health and mental health problems as they move into adulthood. Children from poor families are at increased risk of. Mistry et al. Both externalizing symptoms and internalizing symptoms become more prevalent the longer children have been living in poverty Bolger et al.
In virtually all of the studies reviewed here, investigators take some of these differences into account by including control variables e. Such studies provide better estimates of the effects of poverty on children than studies without such controls. These area-level studies focus on economic stressors outside the family, and may help to establish a causal relationship between economic conditions and mental health.
How- ever, they generally lack the data necessary to explore processes that account for these associations. Research also links increases in state-level unemployment rates to greater prevalence of mental health problems among children, controlling for parental mental health Golberstein, Gonzales, and Meara, Correlational studies that link family income and child mental health have produced evidence suggestive of several mediating mechanisms.
This model hypothesizes that economic hardship induces strain and pressure in parents. The strain. Distressed parents reported feeling less effective and capable in disciplinary interactions with their child and were observed to be less affectionate in parent-child interactions. Mental health problems in childhood and adolescence, especially externalizing behavior problems, warrant efforts aimed at prevention or early treatment because of their high costs to individuals, families e.
Their consequences for outcomes in adulthood include lower edu- cational attainment, higher rates of unemployment, and reduced earnings for a review of these studies, see Currie, This gap appears in virtually all measures of achieve- ment including grades, standardized test scores, high school graduate rates, college attendance, and college graduation rates Bradbury et al. This gap in achievement is present when children enter kindergarten and stays relatively stable over time, indicating that the gap has its origins in the preschool period. Indeed Hair et al. In general, this link is stronger when the dependent variable is serious delinquency Bjerk, ; Farnworth et al.
Dividing household income into quintiles, he found a negative relation- ship between household income and participation in serious crime e. In fact, it appears that the income achievement NCES , gap has been growing for at least 50 years. Hair et al. Blum et al. In addition, the relationship was much stronger if the measure of family economic well-being included other family economic indicators in addition to income e. Household income and participa- tion in minor crime e. Family poverty in middle childhood appears to be less important as an antecedent to serious delinquency than poverty during early childhood or adolescence Jarjoura, Triplett, and Brinker, Several studies show that much of the increased risk of delinquency associated with poverty is mediated through negative parenting and family conflict e.
Several studies have found that official intervention e. Moreover, analyses of panel data suggest that official intervention increases involvement in crime in early adulthood by adversely affecting educational attainment and employ- ment. Declines in educational progress following official intervention may be triggered by stigma and exclusion from school. Increases in crime in response to official intervention appear to be especially pronounced among African American males and males who come from poor family back- grounds Bernburg and Krohn, Delinquency is one of a group of adolescent risky behaviors that are correlated and tend to co-occur Gruber, ; Jessor, Turbin, and Costa, This co-occurrence has stimulated research on domains and pro- files of adolescent risky behavior and their antecedents and outcomes in early adulthood e.
Using data from over 12, adolescents from Add Health, Bartlett, Holditch-Davis, and Belyea identified three clusters of youth mean age Other research distinguishing profiles of risky behavior among adolescents based on delinquency, smoking, drug use, drinking, sexual behavior, etc. Early timing of events beyond sexual debut has been conceptualized as a marker of risky behavior e.
Research confirms that family poverty and low socio- economic status increase the probability of early timing of transition events e. In turn, early timing of transition events has been found to predict significant growth in depressive symptoms in early adulthood Wickrama, Merten, and Elder, ; Wickrama et al.
Early childhood is a period when brain development is rapid, and children are very sensitive to the impacts of family poverty Blair and Raver, Language development diverges for poor and nonpoor children almost as soon as expressive language emerges at 15 or 16 months of age, and by 3 years of age poor children are markedly behind in their language acquisition Hart and Risley, One recent study indicated that differences in language development between poor and nonpoor children can be seen as early as 7 months of age Betancourt, Brodsky, and Hurt, Another study looking at EEG37 patterns found decreased electrical activity in the frontal cortex, the part of the brain that controls executive function, in poor 6- to 9-month-olds compared with those living in families with higher income Tomalski et al.
The impacts of poverty experienced early in childhood last into adulthood. Pov- erty in early childhood has been shown to be a very significant negative pre- dictor of academic performance in school in middle childhood and beyond. The reading and math skills of children experiencing poverty in early life diverge over time from the skills of more advantaged children during the school years Votruba-Drzal, These outcomes are likely to lead to less employment as adults and to intergenerational poverty.
And poverty in early childhood is likely to persist. Nearly one-half of children born to poor parents remain poor for one-half or more of their childhoods Ratcliffe and McKernan, Nevertheless, poverty experienced later in childhood is also associated with negative outcomes in adolescence and adulthood, perhaps in part because the length of time a child spends in poverty is also important. Experiencing persistent poverty for one-half or more of childhood years is associated with not graduating from high school, having teen nonmarital births and nonmarital births as adults for females, and with higher arrest rates by young adulthood for males Ratcliffe and McKernan, The longer the duration of poverty, the more likely the child will have these negative outcomes.
Families living in deep poverty experience even greater material hardship and parenting stress than those who are poor but living between 50 percent and 99 percent of the federal poverty level Mayer and Jencks, Young children growing up in deep poverty have higher rates of obesity, and three times the rate of elevated blood lead levels compared with other poor children Ekono, Jiang, and Smith, One study showed that children in deep poverty had scores 6 to 13 points lower on standardized tests of IQ, verbal ability, and achieve- ment compared with nonpoor children.
Scores for children living in poverty but above deep poverty were also lower than those who were nonpoor, but the differences were not as large Brooks-Gunn and Duncan, Children in deep poverty also are more likely to grow up in families with significant additional risk factors. Compared with poor children not in deep poverty, they are more likely to have parents reporting poor or fair health and mental health, more parental stress, less social support, and living in unsafe neighborhoods Ekono, Jiang, and Smith, These factors predict poor health and development outcomes in children.
The combination of deep poverty and family adversity is particularly toxic Ekono, Jiang, and Smith, Poverty, violence and health: The impact of domestic violence during preg- nancy on newborn health. Aizer, A. The intergenerational transmission of inequality: Maternal disadvantage and health at birth. Juvenile incarceration, human capital, and future crime: Evidence from randomly assigned judges. Almond, D. Killing me softly: The fetal origins hypothesis. Anda, R. The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology and epidemiology.
Bartlett, R. Clusters of problem behaviors in adolescents. Bernburg, J. Labeling, life chances, and adult crime: The direct and indirect effects of official intervention in adolescence on crime in early adulthood. Betancourt, L. Socioeconomic SES differences in language are evident in female infants at 7 months of age. Bjerk, D. Measuring the relationship between youth criminal participation and house- hold economic resources. Blair, C. Poverty, stress, and brain development: New directions for prevention and intervention.
Allostasis and allostatic load in the context of poverty in early childhood. Blum, R. Bolger, K. Psychosocial adjustment among children experiencing persistent and intermittent family economic hardship. Bradbury, B. Achievement Gap in Comparative Perspective. Brooks-Gunn, J. The effects of poverty on children. Cancian, M. The effect of additional child support income on the risk of child maltreatment.
Case, A. Economic status and health in childhood: The origins of the gradient. Chaudry, A. Poverty is not just an indicator: The relationship between income, poverty, and child well-being.
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Chen, E. How low socioeconomic status affects 2-year hormonal trajectories in children. Coley, R. The relationship between economic status and child health: Evidence from the United States. Conger, R. Socioeconomic status, family processes, and individual development. Economic stress, coercive family process, and developmental problems of adolescents. Currie, J. Inequality at birth: Some causes and consequences. Healthy, wealthy, and wise: Socioeconomic status, poor health in childhood, and human capital development.
Chipping away at health: More on the relationship between income and child health. Early-life origins of life-cycle well-being: Research and policy implications. Within-mother analysis of seasonal patterns in health at birth. Socioeconomic status and child health: Why is the rela- tionship stronger for older children? Is there a link between foreclosure and health? Traffic congestion and infant health: Evidence from E-ZPass. Something in the water: Contaminated drinking water and infant health. Canadian Journal of Economics, 46 3. Dashiff, C. Poverty and adolescent mental health. Dubow, E.
Duncan, G. Economic deprivation and early child- hood development. How much does childhood poverty affect the life chances of children? American Sociological Review, 63 3 , Ekono, M. Young Children in Deep Poverty. Elder, G. Children of the Great Depression. Chicago: University of Chicago Press. Elder Jr. Social Institutions and Social Change. Piscataway, NJ: Aldine Transaction. Evans, G. A multimethodological analysis of cumulative risk and allostatic load among rural children. The environment of poverty: Multiple stressor exposure, psychophysiological stress, and socioemotional adjustment.
Measurement in the study of class and delinquency: Integrating theory and research. Gassman-Pines, A. Effects of statewide job losses on adolescent suicide-related behaviors. Gershoff, E. Income is not enough: Incorporating material hardship into models of income associations with parenting and child development. Golberstein, E. Goodman, E. Goosby, B. Gruber, J. Risky Behavior Among Youths. Hair, E. Risky behaviors in late adoles- cence: Co-occurrence, predictors, and consequences. Hair, N. Association of child poverty, brain development, and academic achievement.
Hanson, T. Duncan and J. Brooks-Gunn Eds. New York: Russell Sage Foundation. Hart, B. Haveman, R. Heckman, J. Skill formation and the economics of investing in disadvantaged chil- dren. Iceland, J. Income poverty and material hardship: How strong is the association? Institute of Medicine. Institute of Medicine Jarjoura, G. Growing up poor: Examining the link between persistent childhood poverty and delinquency. Jessor, R. Risk and protection in successful outcomes among disadvantaged adolescents.
Kessler, R. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Khanam, R. Child health and the income gradient: Evidence from Australia. Korenman, S. Socio- economic inequalities in depression: A meta-analysis.
Lorant, V. De- pression and socio-economic risk factors: 7-year longitudinal population study. Ludwig, D. The association between pregnancy weight gain and birthweight: A within-family comparison.
Macomber, J. Mayer, S. Poverty and the distribution of material hardship. McLeod, J. McLoyd, V. Race, class, and ethnicity in young adulthood. Mistry, R. Mitchell, C. Moore, K. Washington, DC: Child Trends. Morris, P. National Scientific Council on the Developing Child Cambridge, MA: Harvard University. Adverse Childhood Experiences. Retrieved September 28, from www. Noble, K. Family income, parental education and brain structure in children and adolescents.
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Sampson, R. Urban poverty and the family context of delinquency: A new look at structure and process in a classic study. Sedlak, A. Department of Health and Human Services. Shonkoff, J. The lifelong effects of early childhood adversity and toxic stress. Strohschein, L. Household income histories and child mental health trajectories.
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Adolescent health risk profiles: The co-occurrence of health risks among females and males. This is based on an indicator known as OECD Family Benefits Pub- lic Spending, which refers to public spending on family benefits, including financial support that is exclusively for families and children.
Spending on health and housing also assists families, but not exclusively, and it is not included in this indicator. Broadly speaking, there are three types of public spending on family benefits: 1. Public spending on child-related cash transfers to families with children, including child benefits or child allowances that in some countries are income-tested; public income support payments for single-parent families; and income support issued during periods of parental leave.
Public spending on services benefits in kind for families with children, including direct financing and subsidizing of providers of child care and early education facilities; public child care support through earmarked payments to parents; public spending on assis- tance for young people and residential facilities; public spending on family services, including center-based facilities; and home help services for families in need.
Financial support for families provided through the tax system, including tax exemptions e. This indicator can be broken down by cash benefits and benefits in kind and is measured in percentage of GDP. This family benefit is intended to assist with the cost of raising children and consists of two parts, A and B. Overall eligibility criteria include these: 1 the parent s have a dependent child or full-time student under the age of 20 who does not receive a pension, payment, or other benefits; 2 the parent s are providing care for the child at least 35 percent of the time; and 3 the family meets a specific income test.
Consequently, it is a near universal benefit and is classified by OECD as such. Part A is given for each child in families that meet the eligibility criteria, and Part B is intended to provide additional assistance to single parents, nonparental caregivers, and couples with one earner.
In order to receive Part A, children must also meet immunization requirements. During this 4-year period, Australia moved from having the 19th-lowest rate to the 7th-lowest rate of child poverty. The UNICEF report highlights that Australia had a multipronged approach, which included countercyclical policies to alleviate the effects of the economic downturn as well as stimulus packages that were targeted to low-income families with children.
In , the indexation of Family Tax Benefit Part A was made less generous, leading to a decline in the number of children benefiting from it, which fell from about 80 percent of all dependent chil- dren in to about 69 percent in Whiteford, This is based on guidance from the European Commission that set out a three-pronged framework to address child poverty.
With regard to adequate resources, Ireland provides a number of dif- ferent income supports for families with children. The government offers a Child Benefit, which is a monthly payment payable to the parents or guardians of children under the age of 16, or under the age of 18 if the child is in full-time education, full-time training, or with a disability and cannot support themselves Ireland, Department of Employment Affairs and Social Protection, The Irish government also has programs intended to make quality child care more affordable and accessible.
Innocenti Report Card Isaacs, J. Nichols, A. Whiteford, P. Australia bucks child poverty trend but the future looks a lot bleaker. The Conversation, October. NOTE: Civilian labor force participation rate for men, annual, seasonally adjusted. NOTE: Percent of women 25 years and over who have completed high school or college. NOTE: Fertility rate for children born per 1, women aged by marital status from to NOTE: Family incomes are adjusted for underreporting. Key behaviors involve labor market employment and hours of work and family structure choices marriage and fertility.
This strong pro-work effect coupled with little evidence of earnings reduction for those already in the labor market strengthens the anti-poverty effect of the program over what it would be if family income only increased by the amount of the initial benefit. Earning reductions weaken the anti-poverty effects of the programs over what they would be if family income only increased by the amount of the benefit. Program-by-program details on our behavioral assump- tions are provided in this appendix, with additional implementation details provided in Appendix F. A smaller research literature attempts to estimate behavioral effects of programs and policies on family structure and childbearing.
As described in Chapter 7, estimates from this research are much more tenuous and variable than those for the effects of programs and policies on labor market behavior. More often than not, no statistically significant responses are found. As a result, the committee did not simulate behavioral responses on family struc- ture and childbearing. We refer to this evidence selectively below. This option was proposed in Giannarelli et al. We adapt their proposal to our data. EITC Policy 2: Increase payments by 40 percent across the entire schedule, keeping the current earnings eligibility range.
Appendix F provides the details of these two proposed policy changes. The credit is phased in at low earnings levels and then phased out at higher earnings levels. The credit is predicted to reduce labor supply for those in the labor market for all but the lowest-earning single parent workers e. However, there is little empirical support for this prediction other than some evidence that self-employed workers adjust to maximize the credit along the phase-in region Chetty, Friedman, and Saez, ; Chetty and Saez, ; Saez, Theory is more complicated for two earner couples, but we expect secondary earners to reduce work effort at the extensive employment and intensive margin hours of work of labor supply.
The research shows small reductions in employment and intensive margin responses for secondary earners and little effect on primary earners Eissa and Hoynes, , To the extent that the EITC increases labor force participation, tax incidence models suggest that the earnings subsidy in the EITC will be shared between the employers and employees. The EITC also creates incentives for low-income one-earner couples to marry and creates incentives for low-income two-earner couples to avoid marriage or separate.
Therefore, the EITC, like ordinary income taxes, creates marriage penalties for some and marriages bonuses for others; these incentives are inherent in a family-based tax system. For marriage, the evidence is largely inconclusive and any effects appear to be quite small Ellwood, ; Herbst, ; Michelmore, ; Rosenbaum, There is less evidence on the effects of the EITC on fertility Baughman and Dickert-Conlin, but again the results suggest small effects.
To incorporate behavioral adjustments into the TRIM3 model, we start by identifying estimates from the literature. Appendix F provides the details of how these assumptions about the magnitude of the behavioral response are implemented in TRIM3. The employment effects of both EITC-based policies are large. These effects constitute a significant contribution to the poverty reduction of the policies.
Policy 1 reduces child poverty from For single working mothers with a child under age 13, median. This policy option was proposed in Giannarelli et al. States that currently use an income limit for child care subsidies that is higher than percent of poverty were assumed to continue using those higher limits.
Behavioral Responses to Expanding Child Care Subsidies A large body of research indicates that government child care subsidy programs increase employment rates among mothers in low-income fam- ilies. Blau and Blau and Tekin report findings from several local-area reforms in the s and early s showing positive impacts on employment.
Studies of the impact of the CCDF, one of the programs in our proposal, have also been conducted or reviewed by Blau and Tekin , Fang and Keane , and Tekin The CCDF was found to increase employment of single mothers by 0. Blau reviewed a large number of studies that had provided estimates of the elasticity of employment with respect to a change in the net hourly cost of child care the latter defined as the out- of-pocket cost of care per hour of work.
The studies he reviewed showed elasticities ranging from We take the midpoint of this range, equal to The additional employment is then distributed randomly across women who, if they began to work, would benefit from the policy. Fur- ther implementation details can be found in Appendix F. The research literature focuses almost exclusively on the impacts of child care costs on employment rather than on hours of work conditional on employment.
We therefore lacked sufficient research evidence to simulate effects at the intensive margin. Both policy options have significant impacts on employment and earn- ings and these are responsible for essentially all of the poverty reduction. In the absence of any employment effects, the two policy options reduce child poverty from its initial The employment effects reduce these rates down to the Policy 1 imposes this increase in all states.
Policy 2 follows Dube , who recommends setting minimum wages to take account of local prevailing wages. But the and 10th percentile wages would be affected in many states; hence the. For example, a tolerance of 25 cents below the minimum wage is used to identify individuals in the Current Population Survey who report a wage slightly below the minimum but who may have simply been misreporting.
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