England Case Study Report

1.1 Introduction

This country report evaluates the long-term effects of the Sure Start program on reducing disparities in educational outcomes in England. Educational inequality, the unequal distribution of educational success and achievement across different social groups (e.g., based on socioeconomic status, ethnicity, or gender), remains a critical issue in England (Sutton Trust, 2021).

These inequalities often emerge early in childhood and can significantly influence an individual’s life chances and social mobility (Heckman, 2006). Prior to the implementation of ECE programmes like Sure Start,evidence of substantial differences in childhood development and educational attainment existed more frequently in England, particularly for children from 13 disadvantaged backgrounds (Sylva et al., 2007).

Sure Start, a major early childhood intervention program launched in 1998, was designed to tackle these inequalities by offering a range of support services to children and families in deprived communities. The program aimed to enhance children’s health, development and preparedness for school, with the ultimate goal of closing the achievement gap.

The present study uses a mixed-methods approach to explore the effectiveness of Sure Start in reducing long term inequalities in educational outcomes. The report consists of three core components: a quantitative analysis examining the program’s impact using longitudinal data from the Millennium Cohort Study (MCS) linked with the National Pupil Dataset (NPD); interviews with policymakers to gain insights into the program’s implementation, operation, and intended mechanisms of action; and a media analysis investigating how perceptions of Sure Start’s effectiveness were reflected in six influential tabloid and broadsheet newspapers throughout the duration of the programme. By integrating these multifaceted perspectives, this report seeks to provide a thorough and nuanced evaluation of Sure Start’s long-term legacy in addressing educational inequality in England, considering both its measurable effects and its broader social and political context.

This case study begins with an overview of the national context, followed by a review of relevant literature and research questions. The methods section details a mixed methods approach consisting of a quantitative analysis, qualitative interviews with policy makers and a qualitative media analysis. The report then presents the results and concludes with a brief summary of main findings and suggestions for future research.

1.2 National Context

The launch of Sure Start

In the realm of social and education policy, Sure Start provides a novel example of how early intervention can shape the trajectory of children’s lives. Launched in England in 1998, the ambitious program sought to provide comprehensive support to families with young children in disadvantaged areas, giving them the best possible start in life. To that end, the intervention aimed to provide a range of services including childcare, early education, health and family support. Local partnerships involving voluntary groups, parents and local authorities were crucial in running the Sure Start local programs (SSLP).

These programs offered core services like outreach, play and healthcare, along with additional services designed to meet local needs. Specifically, Sure Start offered pre- and post-natal healthcare; parenting support through stay-and-play sessions as well as more structured and often evidence-based programmes; early learning and childcare; support to families of children with special educational needs (SEN); and support to parents for (re-)entering employment.

Sure Start’s origins lie in the then Labour government’s emphasis on social inclusion and commitment to tackling child poverty. While drawing inspiration from existing early childhood programs both domestically and internationally, Sure Start aimed to establish a more integrated and holistic support system.

The physical embodiment of the Sure Start programme is Sure Start Children’s Centres (SSCC), which were considered central activity hubs, providing a diverse range of services tailored to the needs of families with children under the age of five.

These centres were not merely childcare facilities but also vibrant community hubs where parents could access support and resources related to child health, parenting and family well-being. From parenting classes and health advice to early learning activities and childcare support, Sure Start aimed to create a nurturing environment where children could thrive. This multifaceted approach reflected the understanding that child development is influenced by a range of interconnected factors, including health, social, emotional, and cognitive development.

Sure Start services and activities

As presented above, Sure Start centres acted as local hubs, offering integrated services tailored to support children under five and their parents. A wide range of activities and services were provided, including early education and childcare, health services such as health visitor clinics and breastfeeding support, parenting programmes, speech and language therapy and family support services. Table 1 illustrates an overview of Sure Start services and activities.

As seen in Table 1.1, Sure Start centres offered stay-and-play sessions, baby groups, advice on nutrition and child development and employment and training guidance for parents. These activities aimed to ensure that children had the best possible start to life by supporting their health, cognitive, social and emotional development. Sure Start aimed not only to help children thrive during their early years but also to strengthen family bonds and reduce inequalities by making services more accessible and coordinated.

Sure Start, at its core, was driven by a profound commitment to reducing inequality in educational outcomes, recognising that the seeds of success are often sown in the earliest years of life. Thus, the program’s focus on early learning and development aimed to equip children with the foundational skills and knowledge they needed to succeed in school and beyond. This focus was informed by research demonstrating the importance of early language development, cognitive stimulation and social-emotional skills for later academic achievement (Heckman, 2006; Melhuish et al., 2010). Sure Start aimed to provide these crucial inputs to children from disadvantaged backgrounds, helping to close the gap in developmental outcomes.

Table 1.1: Sure Start Services and Activities

Source: Carneiro et al., 2025

Building on this national context, the following section reviews existing literature to situate the study within the broader academic discourse and identify relevant research questions.

1.3 Country based literature review

Since its launch, the Sure Start policy has been subject to rigorous evaluation. Researchers have employed a variety of methodological approaches, including wait-list control designs, cross-sectional studies and longitudinal analyses to assess the short and medium-term impacts of Sure Start on child educational outcomes and associated factors.

Concerning the short-term impacts of Sure Start on the behavioural and social competencies of 3-year-old children, two influential cross-sectional studies conducted by the National Evaluation of Sure Start (NESS) team (Belsky et al., 2006; Melhuish et al., 2008) revealed mixed findings. The main results showed that children in Sure Start Local Programme (SSLP) areas revealed better social development than those in non-SSLP areas, with more positive social behaviour and self-regulation.

These findings indicated that the effects vary by degree of family disadvantage. More specifically, children of non-teen mothers showed fewer behavioural problems and greater social competencies when living in SSLP areas than in comparison areas. Along the same line, a study by Melhuish et al. (2010) was undertaken to measure the impact of Sure Start on the development of children at age 5. The findings revealed that children in SSLP areas overall were not showing greater language development by age 5 than children in non-SSLP areas.

An additional quasi-experimental study was implemented by Melhuish et al. (2010) to evaluate the impacts of Sure Start on 5-year-old children and their families. In this study, more than 7000 families in 150 SSLP areas were compared to about 2000 families who participated in Millenium Cohort Study (MCS).

The key findings indicated that there were no differences found between the NESS and MCS groups on several measures of cognitive and social development based on the surveys and reports completed by teachers and mothers. As research studies did not find significant impacts of Sure Start Local Programs on the language development of children in a short-term period, Melhuish et al. (2010) suggested to optimise the quality of the childcare centres to boost the language development of participating children.

Regarding the medium-term impacts of the project, several longitudinal studies were designed and implemented to examine changes in children’s educational outcomes at differing points after several years of engaging in Sure Start. Like previous studies, Melhuish et al. (2012) indicate that there were no consistent effects of SSLPS on children’s development based on a comparison between SSLP and non-SSLP groups at seven years.

Melhuish et al. (2012) explain that one reason for this could be the high levels of 3- and 4-year-old preschool participation due to free Entitlement to preschool across England, which impacted Sure Start and non-Sure Start groups. Interestingly, a recent study conducted by Carneiro et al. (2024) found that access to a Sure Start centre between the ages of 0 and 5 significantly improved the educational achievement of children, with benefits lasting at least until their General Certificate of Secondary Education (GCSEs, age 16).

Children who lived within a short distance (2.5 kilometres) of a Sure Start centre for their first five years performed 0.8 grades better on their GCSEs. These are average impacts across all children living near a centre, regardless of whether those families used the centre. In addition, the study found significant effects of Sure Start on the proportion of children recorded as having special education needs (SEN).

While it increased the likelihood of children being identified with SEN at age 5, it significantly reduced the proportion recorded as having SEN at ages 11 and 16. This means Sure Start may have initially led to more children being identified with SEN, but in the long run, the program could have also reduced the number of children needing ongoing SEN support (Carneiro et al., 2024).

Previous research on Sure Start has yielded mixed findings. While studies have demonstrated significant positive impacts on family support during the early years, effects on immediate child development, including cognitive skills and behaviour, have not consistently shown significant impacts.

However, evidence suggests that the Sure Start programme may have had a more substantial and enduring impact on children’s academic performance in the mid-term, over a decade after its implementation. In other words, previous studies suggest that investing in early years programmes, which offer integrated services like healthcare, social and educational support, may have the potential to yield long-term benefits for children across a range of outcomes.

To further strengthen this evidence base, additional research is necessary to comprehensively assess the long-term impacts of Sure Start on student achievement. This constitutes the primary objective of the present study, which will be examined more thoroughly through hypotheses, research questions and methods in the following sections.

1.4 Research questions/ hypotheses

• Hypothesis: The Sure Start policy increases the chances of disadvantaged students obtaining at least one post 16 qualifications- ISCED level 3 by age 18 (A-level qualification, technical/vocational qualification level 3 or equivalences).
• How was the design, implementation and evaluation of the Sure Start policy perceived by policy makers?
• To what extent does the media content found identify Sure Start as being effective at reducing inequalities in education outcomes?

1.5 Methods

1.5.1 Overall Research Design

Mixed methods, which combine both quantitative and qualitative approaches, were used in our current study. Mixed methods research is a robust way to evaluate and measure the effectiveness of education policies on educational outcomes, as it provides a more comprehensive understanding of the policy at large (Creswell, 2009; Teddlie & Tashakkori, 2009).

This helps researchers understand not only the “what”, but also the “how” and “why” of policy effectiveness. By combining different data sources and methods, researchers can triangulate the findings and increase the credibility of their conclusions. In addition, these research methods help enhance policy relevance by generating findings that are more relevant and actionable for policy makers.
Figure 1 represents an overview of our research design using mixed methods. As illustrated in the figure, we focus on 3 main components in the current study: a quantitative approach, interviews with policy makers and a media analysis. Details are provided in the following figure.

Figure 1.1: Overview of research design

We will now outline the quantitative research data and analytical approach. This will be followed by outlining the qualitative data and methods.

1.5.2 Quantitative research design

For the quantitative section of this report, we conducted a quasi-experimental design to evaluate the long-term impacts of Sure Start on students’ outcomes measured by obtaining at least 1 post 16 level 3 qualification by age 18 (A level qualification, technical/vocational qualification level 3 or equivalences). To that end, we created a variable that contains information on exposure to Sure Start, with non-exposure as the comparison group. This variable is based on the distance between where the student lived when they were 5 years old and the nearest location of a local Sure Start centre.

The main reason we chose this approach is because there is no specific information on who registered to which Sure Start centres. This approach was also undertaken by Carneiro, Cattan, Ridpath (2024) in their latest study measuring the short- and mid-term impacts of the Sure Start policy.

In the current study, we consider all children living within a specified district (a lower layer super output area, LSOA) as being exposed to Sure Start if there was a Sure Start centre within 2 km of the student’s address (measured by the students LSOAs). As explained by Carneiro, Cattan, and Ridpath (2024), this approach is highly policy relevant because Sure Start was an area-based programme open to all local children and their families.

Alternatively, students who lived further away from the 2 km radius belong to the non-Sure Start group. Due to data availability, we also assume that students did not change their address from the time they were born until they were 5 years old.

1.5.2.1 Data (quantitative)

The analysis in the current study uses data from three main data sources:

Data on Sure Start programs and Sure Start centres

We use data from the Department for Education to discern the exact location of Sure Start programmes (SSLPs) and Children` centres (SSCCs) from 1999 to 2006. For some SSLPs that have the same postcodes as SSCCs, we removed the duplicates and assume that the Local programme closed when the children ‘centres opened. In total, 1420 SSLPs and SSCCs from 1999 to 2006 were included in the study.

Data on LSOA where students lived when they 5 years old 21

The Millennium Cohort Study sweep 3 Identifier dataset was used to obtain information on the LSOAs where students lived at age 5 (University of London, Institute of Education, Centre for Longitudinal Studies, 2024). As mentioned previously, there is no data on LSOA where students lived when they were 0-5 years old. Thus, we use the variable LSOA in the MCS sweep 3 Identifier dataset to indicate the LSOA areas where students lived at age 5 and assume that children and their families lived in the addresses when the children were 0-5 years old.

Data on students obtaining post 16 qualifications and equivalences

This study utilises data from the Millennium Cohort Study (MCS) linked to the National Pupil Database (NPD) to examine outcomes related to post-16 educational attainment. The MCS is a nationally representative longitudinal cohort following approximately 19,000 children born in the UK between 2000 and 2001. It includes deliberate oversampling of children from disadvantaged areas and ethnic minority backgrounds to allow for detailed subgroup analyses. The cohort has been surveyed at multiple points throughout childhood and adolescence, with Wave 7 (age 17) conducted in 2018–19.

The MCS–NPD linkage was carried out with parental consent, and matching was conducted using identifiers such as name, date of birth, and postcode across several MCS sweeps. The resulting KS5 linkage, completed in 2022/23, achieved a high match rate of 95.4% among cohort members residing in England. The linked NPD data includes detailed educational records covering Key Stages 1 through 5, pupil characteristics (such as eligibility for free school meals, special educational needs status, and ethnicity), school-level variables, and attendance records.

Although the MCS linked to NPD data contains multiple waves, we used only data from MCS linked to NPD Wave 7 for several reasons. Most importantly, Wave 7 is the only sweep that includes information on Key Stage 5 (KS5) qualifications – namely, ISCED level 3 outcomes such as A-levels and equivalent qualifications. This made it the most relevant and suitable wave for addressing our research question focused on post-16 attainment. Moreover, our current study was designed to answer a specific and straightforward research question without the need to track changes across multiple time points. This was a strategic decision, shaped by both the significant administrative burden of gaining access to longitudinally linked data and the time constraints of the research project.

The linked MCS–NPD dataset wave 7 is uniquely suited for evaluating the outcomes of
policies targeting disadvantaged students. In particular, the oversampling of children from low-income areas aligns with the policy goals of Sure Start, which aimed to improve outcomes for children in the most deprived communities. This allows us to draw a subsample from the dataset to examine whether students from disadvantaged backgrounds (measured by enrolment in free school meals (FSM)) – those most likely to be affected by Sure Start – benefited in terms of post-16 educational attainment.

Access to the linked dataset is restricted and requires researchers to undergo a formal accreditation process and use the UK Data Service Secure Lab, which enforces strict data security protocols. Given the richness of the education variables and the quality of the linkage, the MCS–NPD dataset represents one of the best sources currently available for evaluating long-term educational outcomes in the UK.

1.5.2.2 Sample

The MCS–NPD dataset wave 7 contains data on approximately 7000 students. After merging and cleaning the data, we determined 761 disadvantaged students, as measured by enrolment in free school meals (FSM). These 761 students are included in the current study. Of this, 486 students are in the Sure Start group (63.9%) and 275 students are in the non-Sure Start group (36.1%).

Table 1 in the findings section presents the sample with detailed information on gender, English as an additional language, ethnicity, and children with special education needs (SEN) across both the Sure Start group and non-Sure Start groups.

1.5.2.3 Variables

Outcome

The outcome measured in the current study is a binary variable on obtaining at least one post 16 qualification (either A level qualifications, vocational qualifications at the same level or equivalences) (no=0; yes=1).

Predictor

The main predictor is a binary variable indicating whether the students belong to the Sure Start group or not (no=0, yes=1).

Control variables

When testing the hypothesis, three variables (gender, children with special education needs-SEN, and English as a second language) were added into the model as controls.

Previous studies found that gender, SEN, and English as a second language are contributing factors that affect students’ achievement (Morita et.al, 2016; Workman and Heyder, 2020; Daniel, 2024; Evans et.al., 2016).

1.5.2.4 Data Analyses

Creating Sure Start and non-Sure Start groups

A procedure which includes 3 steps was undertaken to create the Sure Start and non-
Sure Start groups in our study. The details are as follows:

Step 1: the dataset on locations of Sure Start centres was firstly imported into QGIS software within the UDS secure lab environment. The buffer tool was used to create a circular buffer zone with a radius of 2 km around each Sure Start centre. A new variable was derived containing information of which Lower Layer Super Output Aras (LSOAs) fall within these buffer zones. In total, there are 11.218 LSOAs that are within Sure Start centres/programme locations. The new dataset was exported in csv format.

Step 2: The MCS geographical dataset was merged with MCS linked to NPD in SPSS, using the MCS anonymous IDs as the joint variable to receive the variable on LSOAs where students in the selected sample lived.

Step 3: The dataset exported in step 1 was then merged with the merged MCS dataset in step 2 in SPSS, using LSOA as a joint variable. This merge created a new binary variable, indicating whether the LSOA in which each student lived is within 2 km of the nearest Sure Start centre (yes) or further away (no). This variable will be used as the main predictor in our analysis.

Main analysis

A binary logistic analysis was undertaken to measure the effectiveness of the Sure Start policy in improving the chances of disadvantaged students obtaining at least 1 post 16 qualification. SPSS was used to perform the analyses.

We will now move on to the qualitative research, where we will first describe the research designs of the proceeding analyses.

1.5.3 Qualitative research design

The qualitative data involves interviews with policy makers and a subsequent thematic analysis, examining the opinions and perspectives of these stakeholders on various aspects of Sure Start, including its content and goals, beneficiary involvement, data, evaluations and recommendations for future policies. Following the interviews, the second qualitative component of this report considers the media in determining the effectiveness of Sure Start on reducing inequalities in educational outcomes.

1.5.3.1 Interview inquiry with policy makers

To capture diverse perspectives on the policymaking, implementation, and evaluation of Sure Start, semi-structured interviews were conducted with policy makers representing significant domains within early childhood policy and practice.

Participants in our study include Naomi Eisenstadt, the first Director of Sure Start; Dr. Edward Melhuish, Executive Director of the National Evaluation of Sure Start; Baroness Margaret Hodge, Minister of State for Children, Education and Skills (2003–2005); and Baroness Beverley Hughes, Minister for Children, Young People and Families (2006–2009). In total, 3 online interviews and 1 face-to-face interview were conducted from February to April 2025.

All interviews were audio-recorded and transcribed verbatim manually following the completion of the data collection process. A thematic analysis was conducted according to an initial interview guide to gage insights into the development, implementation, and evaluation of Sure Start perceived by the policy makers.

1.5.3.2 Media Analysis

To conduct the media analysis, we began by determining the six most popular newspapers of varying political persuasion within the period of Sure Start. Based on our research, we limited media sources to The Guardian, The Times, The Daily Telegraph, The Sun, the Daily Mirror and the Daily Mail.

To find articles, we searched through various newspaper archives, filtering our results according to the key words “Sure Start,” “Children’s Centre,” and “Sure Start Programme”. The Times and The Guardian have their own digital archive through Gale and ProQuest respectively. Thus, to limit search outputs, we examined these platforms first. When inputting the terms “Sure Start” and “Children’s Centre” into The Times archive and filtering for dates between 1997 and 2015, we found 28 results.

After full text screening, we inputted 13 articles into NVivo for further analysis. We followed this procedure for each subsequent archive, altering some key words slightly, dependent on the number of outputs generated. Upon examining The Times and The Guardian’s archives, we used the Press Reader archive to find articles in The Daily Telegraph, The Sun, the Daily Mirror, and the Daily Mail.

When filtering for these sources and our key words between the years 2000 and 2015, as 2000 was the earliest year we could examine, we discovered 322 articles.

After screening all outputs from the three archives, we inputted a total of 109 articles into NVivo for further coding and thematic analysis. Based on our manual screening of key words and the repetition of words associated with finances, impact, and politics, we decided to employ these are our main themes. Given the magnitude of articles collected and their differing foci, we organized articles into subthemes which will be further examined below.

As our research question critically considers the effectiveness of Sure Start, we examine the media through a nonbiased approach. Therefore, we searched for articles using non-restrictive keywords and conducted a thematic analysis after a full-text screening of all articles.

Our retrospective thematic analysis in the following sections has allowed us to examine and interpret unfiltered patterns of meaning which work to unveil the sentiments of Sure Start throughout the policy (Clarke & Braun 2017).

We will now move on to the results, beginning with the quantitative data followed by the qualitative.

1.6 Results

1.6.1 Quantitative Results

1.6.1.1 Characteristics of the study students

Table 1.2 below illustrates the characteristics of the students in the quantitative component of this study.

Table 1.2: Characteristics of the Sample Comparison of Sure Start group, Non-Sure Start group, and the total sample.

As seen in Table 1.2, the total number of participants is 761, with 486 students in the Sure Start group and 275 students in the non-Sure Start group. All of these students were recoded as being eligible for Free School Meals (FSM). The number of girls is 403 (53%) and the number of boys is 358, accounting for 47% of the total students. Regarding language background, a majority of students reported English as their primary language.

Conversely, 27.1% of the students indicated that English was an additional language spoken in their home. As for ethnicity, the results reveal that over half of the participants (56%) were reported as white and 44% were reported as non-white. Concerning special education needs (SEN), the findings show that 84.1% of the sample were identified as non-SEN and 15.9% were identified as SEN (with or without statement).

1.6.1.2. Effectiveness of the Sure Start policy on improving the chances of disadvantaged students obtaining at least 1 post 16 qualification

To test the hypothesis, a binary logistic regression analysis was conducted to examine the effect of Sure Start on the achievement measures of disadvantaged students by the likelihood of obtaining at least 1 A-level (ISCED level 3 or equivalent) qualification.

Three models were measured. The first model included the main predictor only. The second model included the main predictor and two control variables (gender and SEN). The third model included the main predictor and three control variables (gender, SEN, and English as a second language).

The standard specification was defined as:

$$\ln\left(\frac{P}{1 – P}\right) = \beta_0 + \beta_1 X_{\text{SureStart}} + \beta_k X_k$$

Where:

• $P$: The probability that a student obtains at least one post-16 (ISCED level 3) qualification.
• $\beta_0$: The intercept (constant) of the model.
• $X_{\text{SureStart}}$: The main predictor (dummy variable) indicating whether the student lived within a Sure Start area (1) or further away (0).
• $\beta_1$: The coefficient representing the effect of living within a Sure Start area.
• $\beta_k X_k$: A set of control variables used to adjust the model, including:
  • Gender: Boy (0), Girl (1).
  • SEN Status: Children with Special Educational Needs (1) vs. non-SEN (0).
  • Language (LANG): English as an Additional Language (1) vs. English as a first language (0).

Model 1 (Only the main predictor was added)

The results are presented in Table 1.3. The findings reveal that the overall model was statistically significant, indicating that the policy had a significant impact on improving the chances of disadvantaged students obtaining at least 1 post 16 qualification by age.

More specifically, the results showed that students in the Sure Start group had significantly higher odds of obtaining at least 1 post-16 qualification compared to those in the control group, $B=0.347$, $SE=0.152$, $p < 0.05$. In other words, students who received support under the Sure Start policy were 1.42 times more likely to obtain at least one A-level or equivalent qualification (95% CI for Exp B: [1.050 – 1.905]).

Table 1.3: Effectiveness of the Sure Start policy in improving chances of disadvantaged students obtaining at least 1 post 16 qualification (Basic Model)

N = 761. Notes: Significance: *** 0.001, 0.01, * 0.05 Source: Authors’ computation based on combined STRIDE data metrics.

Model 2 (Gender and children with special education needs (SEN) were added as control variables)

In Model 2, gender and SEN variables were added as control variables (see Table 1.4). The results are consistent with Model 1, showing the positive impacts of the policy in improving the chances of disadvantaged students obtaining at least 1 post 16 qualification by age 18 while controlling for gender and SEN. Details are presented in Table 1.4 below.

Table 1.4: Effectiveness of the Sure Start policy in improving chances of disadvantaged students obtaining at least 1 post 16 qualification, controlling for gender and SEN

N = 761. Notes: Significance: *** 0.001, 0.01, * 0.05

Key Findings from Table 1.4

This multivariate analysis provides a more detailed look at the impact of the Sure Start policy by controlling for individual characteristics (Gender and SEN status).

SEN Status: Special Educational Needs (SEN) status is the strongest predictor in the model. The high Odds Ratio (4.549) highlights a substantial gap in outcomes between non-SEN and SEN students, emphasizing the importance of controlling for this variable to isolate the true effect of the Sure Start policy.

The Sure Start Effect: With an Odds Ratio (Exp B) of 1.490, students living within a Sure Start area are nearly 1.5 times more likely to obtain at least one post-16 qualification compared to those living further away.

Statistical Significance: The result is statistically significant ($p = 0.013$), meaning the positive correlation is unlikely to be due to chance.

The Gender Gap: Gender is a strong predictor ($p < 0.001$), with girls (coded as 1) having significantly higher odds (1.933) of achieving a qualification than boys.

• The Sure Start Effect: With an Odds Ratio (Exp B) of 1.490, students living within a Sure Start area are nearly 1.5 times more likely to obtain at least one post-16 qualification compared to those living further away.
• Statistical Significance: The result is statistically significant ($p = .013$), meaning the positive correlation is unlikely to be due to chance.
• The Gender Gap: Gender is a strong predictor ($p < .001$), with girls (coded as 1) having significantly higher odds (1.933) of achieving a qualification than boys.
• SEN Status: Special Educational Needs (SEN) status is the strongest predictor in the model. The high Odds Ratio (4.549) highlights a substantial gap in outcomes between non-SEN and SEN students, emphasizing the importance of controlling for this variable to isolate the true effect of the Sure Start policy.

Model 3 (Gender, children with special education needs (SEN), and English as an additional language were added as controlling variables)

In Model 3, gender, SEN, and English as an additional language were added as control variables (see Table 1.5).

Table 1.5: Effectiveness of the Sure Start policy in improving chances of disadvantaged students obtaining at least 1 post 16 qualification, controlling for gender, SEN, and English as an additional language

N = 761. Notes: Significance: *** 0.001, 0.01, * 0.05

Key Findings from Table 1.5

In Model 3, the inclusion of Language (LANG) as a control variable provides a nuanced perspective on the policy’s effectiveness:

• Sure Start Effect: When controlling for language, the Odds Ratio for the main predictor decreases to 1.150, and the result is no longer statistically significant ($p = 0.406$). This suggests that much of the previously observed effect may be intertwined with the demographic distribution of students with English as an additional language.
• Language Impact (LANG): Having English as an additional language is a highly significant predictor ($p < 0.001$). Students in this category (coded as 1) have 2.737 times higher odds of obtaining a qualification than those with English as their first language.
• Consistency of Other Variables: Both Gender and SEN status remain highly significant predictors, consistent with the previous models, reinforcing that girls and non-SEN students generally have better outcomes in this sample.

The results continue to show positive impacts of the policy in improving the chances of disadvantaged students obtaining at least 1 post 16 qualification by age 18 while controlling for gender, SEN, and English as a second language.

To be more specific, students in the Sure Start group had higher odds of obtaining at least 1 post 16 qualification compared to those in the control group, $B=0.140$, $SE=0.169$. However, this result has become non-significant ($p > 0.05$). The reason could be due to the substantial imbalance between the Sure-Start and non-Sure Start groups regarding English as a second language.

A sample with more balanced composition and larger sizes between Sure-Start and non-Sure Start groups is needed for future research.

Model fit comparison

Table 1.6 below illustrates model fit measured by the Chi-square statistic from the Omnibus Test of Model Coefficients, the -2 Log Likelihood (-2LL), and Nagelkerke’s $R^2$.

The Chi-square test assesses whether each successive model provides a statistically significant improvement in fit compared to the previous one (Field, 2018). The -2 Log Likelihood indicates model deviance, where lower values suggest better fit (Hosmer, Lemeshow, & Sturdivant, 2013). Nagelkerke’s $R^2$ is a pseudo-$R^2$ measure that adjusts Cox and Snell’s $R^2$ to scale between 0 and 1, offering an interpretable estimate of the variance explained by the model (Nagelkerke, 1991).

Model Fit and Statistical Validity

The table below illustrates the model fit measured by the Chi-square statistic ($\chi^2$) from the Omnibus Test of Model Coefficients, the -2 Log Likelihood (-2LL), and Nagelkerke’s $R^2$.

• Chi-square ($\chi^2$): This test assesses whether each successive model provides a statistically significant improvement in fit compared to the previous one.
• -2 Log Likelihood (-2LL): This indicates model deviance. Lower values suggest a better fit between the model and the observed data.
• Nagelkerke’s $R^2$: A pseudo-$R^2$ measure that offers an interpretable estimate of the variance explained by the model, scaling between 0 and 1.

Table 1.6: Model Fit Statistics for Binary Logistic Regression Evaluating the effectiveness of the Sure Start policy in improving chances of disadvantaged students obtaining at least 1 post-16 qualification.

Notes: $\chi^2$ values are from the Omnibus Tests of Model Coefficients. –2LL = –2 Log Likelihood. Nagelkerke $R^2$ is a pseudo-$R^2$ estimate of explained variance.

As seen in Table 1.6, Model 1, which includes only the main predictor, is statistically significant, $\chi^2(1) = 5.21$, $p = 0.022$, indicating that the predictor contributed significantly to the model. However, the model accounted for a small proportion of the variance, as indicated by Nagelkerke’s $R^2 = 0.009$, and the -2 Log Likelihood was 1037.36.

Model 2, which added gender and SEN as control variables, showed a significant improvement in model fit, $\chi^2(3) = 80.52$, $p < 0.001$, with a decrease in -2 Log Likelihood to 962.06 and an increase in explained variance (Nagelkerke $R^2 = 0.135$).

Model 3, which includes English as a second language, further improved model fit, $\chi^2(4) = 108.62$, $p < 0.001$, with a final -2 Log Likelihood of 933.95 and a Nagelkerke $R^2$ of 0.178, indicating that this model explained approximately 17.8% of the variance in the outcome.

These results suggest that Model 3 provides the best fit, explaining the most variance (17.8%) with the lowest -2LL.

In sum, the main aim of the current study was to measure the long-term impacts of the Sure Start policy in reducing inequality in educational outcomes measured by the likelihood of disadvantaged students obtaining at least 1 post 16 qualification by age 18 (either A-level qualification, technical/vocational qualification level 3 or equivalences).

The results revealed that the policy has some positive impacts on the probability of disadvantaged students obtaining at least 1 post 16 qualification, but this becomes non-significant in the final model (Model 3). The findings are partly consistent with previous studies which reveal the short and mid-term impacts of the Sure Start policy in improving students’ achievement in general, in supporting disadvantaged children and reducing inequality in education in particular (Melhuish et al.,2008; Carneiro et al., 2024).

A methodological limitation of this study is its omission of a Difference in Difference approach for measuring the long-term impacts of Sure Start. Although we recognise the significance of using a Difference in Difference approach in this research, we were unable to implement it due to the nature of the dataset and time constraints. Further research using a suitable dataset (e.g., National Pupils Database) that allows for the use of DiD is recommended.

Upon the completion of the quantitative components of this study, we will now explore the qualitative results.

1.6.2 Qualitative Results

To determine the effectiveness of Sure Start on mitigating educational inequalities, we have examined it through multiple and varying lenses. While the quantitative data reveals significant insights into Sure Start’s effectiveness, the interviews provide a deeper context, offering a nuanced understanding of the program’s structure, evolution, and impact.

In the first section of this qualitative inquiry, we provide a brief overview of our interview participants and discuss their involvement in Sure Start. We then move on to the content and goals of the policy and its implementation, including components like beneficiaries, data, evaluation, and recommendations for future ECE policies. Through this, we will further evaluate the effectiveness of Sure Start in mitigating educational inequalities.

Role of the Interviewee

Each of our interview participants represents significant early childhood domains. This section includes responses from Naomi Eisenstadt, the First Director of Sure Start, Edward Melhuish, the Executive Director of the National Evaluation of Sure Start, Baroness Margaret Hodge, the Minister of State for Children, Education and Skills from 2003 to 2005, and Baroness Beverley Hughes, the Minister for Children, Young People and Families from 2006 to 2009. Due to the range of interview participants, we have attempted to provide insights into the organisational, academic and political factors that encompass effective policymaking.

Content and Goals of Sure Start

Sure Start was informed by evidence-based practices that had proven effective for children facing multiple disadvantages. The United States’ Head Start policy provided a model for Sure Start, but local initiatives were also significant to its foundation (Glass 1999). As leader of the Islington Council, Baroness Margaret Hodge contributed, in part, to the structure of the Sure Start Programme, as she and other leaders merged nursery education with the care sector, implementing a novel, cross-sectoral approach to early childhood programs which would later become foundational to Sure Start (M. Hodge, Retrieved from interview data). Baroness Hodge also appointed the First Director of Sure Start, Naomi Eisenstadt, who was recruited for the role based on her previous involvement in the voluntary sector and front-line work with impoverished children and families.

In her interview Eisenstadt states that her earlier work influenced her role in Sure Start “in huge ways” as she was already familiar with the people, strengths, weaknesses and complexities of the voluntary sector (N. Eisenstadt, retrieved from interview data). The goals of Sure Start, as stated by all interview participants, were to narrow the gaps between impoverished children and their more affluent peers, giving them a more equal start to life.

Although the goals of Sure Start were shared, its design was more challenging, as its cross-sectoral, multi-organizational and relatively non-uniform approach led to diverging opinions and perspectives. Initially, Sure Start was disseminated into 250 local programmes, aiming to reach one third of children living in poverty in England (Eisenstadt, retrieved from interview data). From a political domain, Baroness Hughes reflects on the difficulties of overseeing such a massive programme with few universal factors.

She notes that Sure Start’s locally based model was paramount to beneficiary involvement but also unsustainable and had to move in a centralized direction to increase effectiveness (B. Hughes, Retrieved from interview data). Melhuish shared similar sentiments, expressing the difficulties of evaluating 250 separate Sure Start programmes with few centralised features.

Upon the first evaluation of Sure Start by Melhuish and his team, it was discovered that Sure Start Local Programmes produced mixed effects, with some adverse outcomes for the most disadvantaged children and their families (Melhuish et al., 2010). Given these results, additional research evidence by Melhuish, and concerns regarding its lack of uniformity, Sure Start Local Programmes were developed into Children’s Centres, centralized hubs offering consolidated services to families.

Deliberation with Beneficiaries and Stakeholders

The switch from Sure Start Local Programmes to Children’s Centres, despite being agreed upon, was met with contention. With this switch, some individuals like Baroness Hughes pushed for validated parenting programmes to establish a more concrete structure to Sure Start (Hughes, Retrieved from interview data). Others, like Baroness Hodge during her time as Children’s Minister, pushed for educational advancements through the implementation of teacher qualifications. Almost all interviewees recalled their concern for the community-centric nature of Sure Start, contemplating the effects of Children’s Centres on community involvement and beneficiary input.

Parental participation was integral from the outset of the policy. Although parental involvement was voluntary and dependent on specific Sure Start localities, many Sure Start boards included parents, who provided invaluable perspectives on local needs. In the interview with Eisenstadt, she emphasizes the significance of beneficiary involvement, stating that “the ideal Sure Start from the very beginning was run by a local board that included health, education and social care, but also had to include local parents” (Eisenstadt, Retrieved from interview data). Eisenstadt also emphasises the successes of these methods, noting that the inclusion of parents in conversations about Sure Start ensured that policymakers and leaders were required to consider diverse perspectives without judgement (Eisenstadt, Retrieved from Interview data). At the time, especially, but also now, this approach to policymaking is infrequent, yet can be remarkably impactful, especially considering local need.

The focus on parental involvement also led to soaring levels of trust amongst family members and Sure Start programme leaders, as families saw first-hand the effects of their personal contributions through specific local programmes. All the interviewees’ remark on the positive reception of Sure Start by parents and families, recalling conversations with parents who stated that Sure Start had changed their lives and positively altered their futures. Eisenstadt attributes the positive sentiments of Sure Start to its core values of dignity and respect, noting that many of the parents involved in the program had never been listened to previously, and Sure Start was one of the first policies where they were treated as informed humans. Baroness Hodge states that Sure Start “was a very empowering program” (Hodge, Retrieved from Interview data). Despite contentions regarding the switch to Children’s Centres, these ultimately led to more positive outcomes for children and their families. However, as mandatory quotas for Children’s Centres increased, especially during the 2008 financial crisis and impending recession, many localities began referring to existing programs as Sure Start Children’s Centres without altering any content or operations. For this reason, Melhuish urges caution when evaluating the effects of Sure Start Children Centres, which remarkably remain positive despite the inclusion of these inefficient non-Sure Start centres in actual Sure Start Centre data.

Data

Sure Start was structured according to a cross-sectoral model, including components of health, education and social work. Given the array of organizations and individuals involved, almost all interviewees expressed the difficulty of data cultivation. Many interviewees explained that it was particularly difficult to obtain data from the health sector, noting the complexity of the organization in general. As the health sector was often the first point of contact between families and programme leaders, the rigidity of this sector, in terms of data sharing, was detrimental.

Despite the difficulties of data sharing, the interviewees note the cruciality of data regarding outreach methods to the most impoverished families and their children. Upon the first evaluation of Sure Start, there was evidence that the most impoverished children and their families were being adversely affected by close proximity to Sure Start Local Programmes. Therefore, stakeholders began using data to more effectively target children with the most needs. This method, referred to as assertive outreach, led to the increased involvement of children facing the most disadvantages in Sure Start programmes (Eisenstadt, Retrieved from interview data). Eisenstadt mentions that the lack of data sharing from the health sector also made this assertive outreach difficult (Eisenstadt, Retrieved from interview data).

Additionally, data was significant for evaluation procedures. Melhuish explains that the Millenium Cohort Study data made the Sure Start evaluations possible, as this data allowed for direct comparisons between Sure Start and Non-Sure Start participants, in an absence of randomised controlled trials (Melhuish, Retrieved from Interview data).

Evaluations

One of the key debates around Sure Start was the use, or lack of, randomised controlled trials. Melhuish advocated for RCTs early on, calling them “the most powerful research strategies to convince other people that the programme is working” (Melhuish, Retrieved from interview data). However, RCTs conflicted with Sure Start’s localised structure.

Eisenstadt explained, in regard to randomised controlled trials, that “there is no way you could have had parental involvement in deciding what to deliver and also have a randomised controlled trial because you have to have the same inputs” (N. Eisenstadt, Retrieved from Interview data).

In other words, standardisation would have undermined the programme’s adaptability. Additionally, from a political standpoint, randomised controlled trials were difficult, as almost all members of parliament wanted a Sure Start Centre for their constituents given the popularity of these programmes (Melhuish, retrieved from interview data). Baroness Hughes notes that, many policymakers “thought it was unethical to conduct randomised controlled trials,” because this would deny children the essential resources that Sure Start Centres provided (Hughes, Retrieved from Interview data).

Ultimately, Melhuish and his team adopted a robust methodology using longitudinal data from the Millennium Cohort Study. As the interviewees reflected on the decision to use an alternative method to potentially more effective randomized controlled trials, Melhuish, Eisenstadt, and Baroness Hughes agree that the methodologies implemented were the most adequate for evaluating the Sure Start model.

Recommendations for Future Policies

When reflecting on Sure Start, the interviewees communicated immense pride. Although evidence of Sure Start’s effectiveness is varied, it was one of the first major early childhood education programmes with popular support from communities, policymakers and various other sectors. Baroness Hodge noted that Sure Start had exceeded her expectations, especially given the evidence of long-term benefits.

She states that the reason for Sure Start’s departure was not in its structure but in the government’s refusal of statutory enshrinement, especially through long-term budget commitments for the programme (Hodge, Personal Communication, 2025). In her reflections, Baroness Hughes claims that with its resources, she is unsure of how Sure Start could have been done differently and remained as effective as it was (Hughes, Personal Communication, 2025).

Looking ahead, the interviewees recommend preserving Sure Start’s core elements. Baroness Hughes and Melhuish argued that children’s centres should remain central to ECE initiatives, urging expert oversight for the most effective development of these policies (Melhuish, Personal Communication, 2025). Eisenstadt emphasized the necessity of early childhood policies that target the youngest children. She suggests enrolling children in interventions as early as birth and following up throughout various stages (Eisenstadt, Personal Communication, 2025). She recognizes, however, that this approach would require extensive data sharing, which she also claims will increase the success of impending policies.

Baroness Hughes shares similar sentiments, urging the government to act with clarity when establishing ECE programs, especially after reflecting on the relatively non-uniform Sure Start model (Hughes, Personal Communication, 2025). She also emphasizes a fundamental commitment to the integration of core services at the local level, with a focus on closing the increasingly widened gap between disadvantaged children and their peers. Baroness Hodge recommends building from the Sure Start model, ensuring that it is enshrined into legislation, so it is not eliminated contingent on fluctuations in funding (M. Hodge, Personal Communication, 2025).

To conclude, the interviews with policy makers reveal the intricacies, complications, and benefits of Sure Start. With its evidence and results driven model, Sure Start not only produced significant short and long-term behavioural, academic and achievement results, but also led to improvements for children and families facing multiple disadvantages.

Although Sure Start faced challenges, all the interviewees noted that there had never been a more popular and well-received intervention for disadvantaged families in England.

We will now move to our final section, the media analysis

1.6.3 Media Analysis

So far, we have evaluated the effectiveness of Sure Start on reducing educational inequalities using quantitative approaches and qualitative interviews. Each method has allowed us to understand Sure Start from diverse perspectives, with the raw numbers and the program’s critical stakeholders working together to provide robust findings. We will now consider a final perspective through our media analysis of newspapers following the implementation and dissemination phases of Sure Start.

Regarding public policy, the media plays a significant role as an agenda setting platform and vehicle for mass information (Saraisky 2016). The media is not only rooted in specific political and economic contexts but also builds upon and subsequently informs public opinion (Saraisky 2016).

Thus, media can reveal the conversations, discourses and themes which everyday people encounter and contribute to most. Through the inclusion of a media analysis in this case study report, we will consider the final perspectives, of the public, politicians and journalists in determining the effectiveness of Sure Start on mitigating educational inequalities.

The analysis results reveal the three most frequent themes including impact, finances and politics. The articles within these themes consider various perspectives, allowing us to analyse Sure Start through the eyes of its most fervent supporters and opponents. As these articles include diverse opinions and stories, they are organized according to subsections, which will be outlined in detail below.

Impact

To begin, we examine impact. As indicated in Table 1.7, articles on impact were largely split between discourses on the positive and negative effects of Sure Start.

Table 1.7: Qualitative Subthemes and Frequent Codes Analysis of subthemes emerged within the “impact” theme, including most frequent codes and keywords.

Key Insights from the Qualitative Analysis

This thematic analysis highlights the diverse range of perspectives found in the literature and reports regarding Sure Start’s impact:

• Critique vs. Praise: There is a notable frequency of critical terms such as “critics/criticism” (21) and “failures” (14), indicating a significant debate in the source material regarding the program’s effectiveness.
• Targeting Concerns: One of the most frequent negative codes, “attracted affluent parents” (9), suggests a challenge in the program’s ability to exclusively reach the most disadvantaged families.
• Foundational Support: On the positive side, the keywords “help” (9) and “support” (5) reinforce the core mission of the centres as vital support hubs for those who engaged with them.

Articles expressing positive sentiments were found in The Times, The Sun, The Guardian, and the Daily Mirror. Thus, positive outcomes were not confined to one news platform.

These articles included success stories regarding the improvement of communal relations, parental testimonials and the promotion of diversity and inclusion as best practice. For example, a journalist for The Guardian writes about one mother’s testimony, stating “Single parent Nancy Silk says Sure Start gave her the confidence to find work and provided the support she needed to get over her depression” (Andalo 2003). There were more stories like this, where families, especially mothers, expressed their admiration for Sure Start. Although many benefits of Sure Start were discussed, there was no mention of positive academic outcomes for children facing disadvantages or for children more generally, which is significant when considering our research questions.

Alternatively, articles regarding the negative effects of Sure Start focused mostly on its failure to reach the most deprived families and to effectively target children facing intersectional disadvantages. Most articles in this subgroup were centred on the middle class’s “invasion” of Sure Start, as individuals expressed complaints that those of a higher socioeconomic status were stealing Sure Start’s resources from families who needed them most. Unlike articles centred on the positive effects of Sure Start, those on its failures focused on the absence of educational benefits afforded to children from disadvantaged backgrounds compared to their peers. An article in The Daily Telegraph states “More than £25 billion spent on early education under Labour has failed to improve children’s language and numeracy skills, according to a study published today” (Paton 2010).

Some articles held more neutral sentiments regarding the effects of Sure Start. Most of these articles, found in The Guardian, offered relatively non-biased examinations of Sure Start as it existed throughout various stages and time periods.

Almost all of these articles had an interrogative tone but claimed that more time needed to pass before evaluating Sure Start on its effectiveness or lack thereof. In an article for The Guardian one journalist writes “Geof Rayner, chairman of the UK Public Health Association, says Sure Start is refreshing precisely because it favours local initiative over central direction. ‘It is always looking to innovate, and it is empowering the people who work in it,’ he says.

It is too early, however, to tell whether Sure Start will have a demonstrable effect on children and families” (“Far from certain” 2003).

In general, articles in this section were largely polarized according to positive and negative sentiments of Sure Start. Articles that expressed positive sentiments contained stories of successful communal and parental outcomes, omitting, for the most part, the benefits afforded to the children involved. The articles which expressed negativity focused significantly on educational outcomes for children, as journalists, researchers and politicians claimed that Sure Start was failing the children who it intended to target.

Finances

Table 1.8: Subthemes and Frequent Codes within the “Finances” Theme Summary of the most referenced terms highlighting the impact of funding cuts, political controversy, and broader social consequences.

Key Insights: The Financial and Political Context

The analysis of the “Finances” theme reveals a high level of public and political engagement regarding the sustainability of Sure Start:

• The Scale of Disruption: The high frequency of terms like “Services” (56), “funding cuts” (24), and “closed” (15) reflects a period of significant austerity where the physical presence of the centres was under threat.
• Human Impact: The most frequent codes in the entire dataset are “Children” (82) and “Families” (63), emphasizing that the financial debate was primarily framed around the well-being of vulnerable citizens in deprived areas.
• Political Polarization: The data shows a heavily politicized environment, with frequent mentions of “Council” (44), “Government” (37), and specific political actors (“Cameron”, “Labor”, “Tory”). The term “protest” (17) suggests that the financial decisions met significant public resistance.

Regarding the first subtheme, addressing the scale and nature of the financial reductions, media coverage frequently emphasises the widespread closure of Sure Start centres nationwide. In an article for The Daily Telegraph, a journalist states “in a survey by 4Children and the Daycare Trust, centre managers estimated that 250 [Sure Start Children’s Centres] will have to close in the next year, 2,000 will provide a reduced service, 3,100 will have a smaller budget, and staff at 1,000 centres have been issued with ‘At Risk of Redundancy’ notices” (Beckford 2011). This reduction in services often included scaled-back provision of activities, playgroups, clinics, support groups and lessons.

The second subtheme focuses on the direct impacts of Sure Start cuts on vulnerable children and families. According to the sources, these cuts are seen as hitting the most vulnerable groups of society hardest. Children and families, especially those who live in deprived areas, rely on Sure Start as a “lifeline” and a crucial support network.

The cuts are framed as a direct hit on them. Consequently, concerns are raised about the impact on children’ chances and the potential increase in child poverty as a result. Furthermore, the cuts are explicitly linked to increased hardship for parents, particularly mothers, to work or afford childcare.

The third subtheme concerns the political controversy and protest surrounding the reduction of Sure Start funding. The debates over Sure Start cuts are highly politicized. Accordingly, David Cameron is accused of breaking pledges to safeguard or improve Sure Start.

Ministers have insisted there is enough money for councils to keep centres open. Nevertheless, the cuts have sparked numerous protests by parents and campaign groups, including marches, teddy bear picnics and themed events, highlighting the importance of the centres. Describing some of the protests, a journalist for The Sun states “Mums and dads brought paddling pools on to the streets of Newcastle to show their anger over planned 65% cuts to children’s centre budgets” (Thompson 2014).

Local councils, many Labour-led in hard-hit areas, describe struggling with the required savings and blame central government cuts. Political perspectives on Sure Start remain divided, with some commentators questioning its efficacy or arguing that it disproportionately benefits middle-class families.

The fourth subtheme explores the broader consequences of budget reductions. As reflected in the social media articles, the reduction in Sure Start services undermines early intervention efforts, which are seen as vital for addressing the root causes of on-going challenges regarding social inequality. Sure Start centres were valued as community facilities and social hubs. Their closure is described as ripping the heart out of some communities. In addition, other local services were cut alongside Sure Start.

An article in The Daily Mirror outlines these cuts, noting: “Other measures include axing child benefit for 830,000 middle-income families, a freeze in Working Tax Credits – which will hit 80% of families – and cuts to the Sure Start programme” (Beattie, 2010).

Politics

Table 1.9 below illustrates the subthemes and most frequent codes for the “politics” theme. Although there are significantly fewer articles encompassing this theme than those regarding impact or finance, politics still encapsulate many conversations about Sure Start in the media. As indicated by the prevalence of newspaper articles about Sure Start, in general, it was widely revered as a universally popular program. Thus, numerous politicians ensured that they had a say in debates on Sure Start and its impending status.

Table 1.9: Subthemes and Frequent Codes for the “Politics” Theme Summary of keywords highlighting the political campaigning, debates on early childhood strategy, and the role of the Labour government.

Key Insights: The Political Landscape (English)

The political analysis confirms that Sure Start was a “widely revered and universally popular program,” which made it a high-stakes topic for politicians across the spectrum:

• Election Tool: The frequency of terms like “Campaign” (4), “Election” (4), and “Minister” (19) shows that Sure Start was frequently used as a benchmark for social policy during political races.
• Social Mobility: Interestingly, the term “Social mobility” (7) appears as a key keyword in debates, highlighting that the program was viewed not just as childcare, but as a strategic tool for long-term societal change.
• The Labour Legacy: With “Labour” (31) being the most frequent term in this theme, the data reinforces the program’s strong association with the Tony Blair era and the subsequent debates about its status under the Coalition government.

Similarly to articles categorised under finance, politicians used newspapers to express their concerns regarding Sure Start funding and potential budget cuts. These individuals often used media for their campaign agendas, comparing promises to uphold and save Sure Start to their opponent’s likelihood of slashing the program.

For example, one journalist for The Sun discusses a political candidate, writing “Only his party would protect Sure Start centres and libraries from cuts, he vowed, as the parties began campaigning ahead of the May 5 polls” (“Clegg’s rallying call” 2011). In general, the political campaign articles that centred on Sure Start advocated for the expansion of welfare in mitigating the gap between the rich and the poor.

Additionally, Sure Start was positively regarded in the political realm as a beacon of the Labour government. When discussing the achievements of Tony Blair’s government, Sure Start was often included as a success.

These sentiments were most often expressed, however, in articles campaigning for the election of proceeding Members of Parliament, which likely represent biased perspectives of Labour and its programmes.

Apart from campaigning, political leaders also used media to debate early childhood policies more generally. These debates were centred on opinions regarding best practice before and after the recession and major cuts to social services. These debates and concerns for early childhood education indicate its widespread reach and universally accepted importance, as more individuals were not only discussing early childhood but increasingly considering the most effective programs for children.

This media analysis has highlighted the diverse opinions and sentiments surrounding the Sure Start initiative, reflecting contrasting views held by journalists, politicians and the wider public. While Sure Start received significant praise, it also faced sustained criticism, often rooted in concerns over funding, effectiveness and political motivations.

Nonetheless, the continued volume of media coverage and public discourse
surrounding Sure Start, and early childhood education programs more generally, underscores their growing recognition as vital components of child development and
social policy. Despite some contestation, Sure Start has undeniably contributed to raising awareness of the importance of early years support and remains a key point of reference in debates about equity and intervention in early childhood.

1.7 Discussion and Conclusion

This country report has evaluated the long-term impacts of the Sure Start policy through a mixed-methods approach, combining quantitative analysis using longitudinal data, qualitative interviews and a media analysis.
The quantitative findings indicate that Sure Start had a positive, but very modest, effect on the likelihood of disadvantaged students attaining at least one post-16 qualification (e.g., A-level qualification, technical/vocational qualification level 3 or equivalent).

However, this effect becomes statistically non-significant in the final model, which controlled for a broader set of variables. This issue may be due to methodological limitations, such as a relatively small sample size (N = 761) and imbalances in group composition, particularly the underrepresentation of English as an Additional Language (EAL) learners in the non-Sure Start group.

An alternative explanation could be that the observed decline in statistical significance may reflect a reduced policy effectiveness over time, with impacts less pronounced in the long term compared to the more pronounced short- and medium-term effects found in previous studies (Melhuish,

Belsky, Leyland, Barnes, 2008; Carneiro, Cattan, Ridpath, 2024. Further research with a larger sample size and more balanced group representation is recommended to better capture the sustained effectiveness of the policy.

The qualitative interviews provided valuable insights into the perceived benefits and challenges of the Sure Start policy from the perspectives of policymakers. Interviewees highlighted Sure Start’s evidence-informed, outcomes-driven approach, which they associated with meaningful improvements in children’s academic achievement, socio-emotional development and family well-being.

Despite some implementation challenges, interview participants expressed that Sure Start was one of the most well-received and impactful interventions for disadvantaged families in the context of early education in England.

According to the interviewees, the broad popularity of Sure Start was due, in part, to the programme’s critical foundation of dignity and respect, which informed beneficiary involvement and community interventions. Although evidence of Sure Start’s effectiveness indicates mixed results, the interviewees help to reveal that Sure Start was more than its nuanced empirical results, as it led to a shift and increasing focus on the significance of early childhood programs in policy education and public realms.

The media analysis further contextualized public and political perceptions of Sure Start. Coverage revealed a spectrum of views: while many stakeholders including journalists, advocacy groups and members of the public praised the policy for its ambitious aims and reported successes, others expressed scepticism regarding its sustainability, funding mechanisms and political motivations.

Nonetheless, the continued prominence of Sure Start in media discourse, and its frequent citation in debates on early years support, reflect its significance as a reference point in discussions about equity social mobility and education reform.

Taken together, these findings suggest that while the long-term measurable impacts of Sure Start in reducing inequalities in educational outcomes may be less robust than initially hoped, its broader influence, both practical and symbolic, remains significant. The policy appears to have laid important groundwork for future efforts to address inequality in early childhood education and continues to inform contemporary policy discourse.

Future research is needed to overcome the methodological limitations identified in this study by employing larger, more representative samples and longitudinal designs that better capture sustained impacts over time. In addition, further evaluations with other early years initiatives/reforms aimed at reducing inequalities in educational outcomes are also recommended. This helps to deepen understanding of the conditions under which such policies succeed or falter.

Glossary