What has happened to Sub-Regional Public Sector Efficiency in England since the Crisis?

Contributor Notes

Authors’ E-Mail Addresses: sbeidasstrom@imf.org

This paper estimates public sector service efficiency in England at the sub-regional level, studying changes post crisis during the large fiscal consolidation effort. It finds that despite the overall spending cut (and some caveats owing to data availability), efficiency broadly improved across sectors, particularly in education. However, quality adjustments and other factors could have contributed (e.g., sector and technology-induced reforms). It also finds that sub-regions with the weakest initial levels of efficiency converged the most post crisis. These sub-regional changes in public sector efficiency are associated with changes in labor productivity. Finally, the paper finds that regional disparities in the productivity of public services have narrowed, especially in the education and health sectors, with education attainment, population density, private spending on high school education and class size being to be the most important factors explaining sub-regional variation since 2003.

Abstract

This paper estimates public sector service efficiency in England at the sub-regional level, studying changes post crisis during the large fiscal consolidation effort. It finds that despite the overall spending cut (and some caveats owing to data availability), efficiency broadly improved across sectors, particularly in education. However, quality adjustments and other factors could have contributed (e.g., sector and technology-induced reforms). It also finds that sub-regions with the weakest initial levels of efficiency converged the most post crisis. These sub-regional changes in public sector efficiency are associated with changes in labor productivity. Finally, the paper finds that regional disparities in the productivity of public services have narrowed, especially in the education and health sectors, with education attainment, population density, private spending on high school education and class size being to be the most important factors explaining sub-regional variation since 2003.

I. Introduction

“The thicket of complexity that exists between central and local [public sector] structures and diffusion of funding and advisory energies leads to unnecessary hurdles for those striving to translate ideas to job creating businesses.”

Sir Witty, 2013

This paper seeks to address the following questions: (i) Has public sector efficiency or productivity at the sub-regional level improved or weakened in England during the fiscal consolidation of 2010–14? (ii) What has been the pattern across different sectors and sub-regions? (iii) Have sub-regions with lower initial levels of efficiency experienced stronger gains, implying some catch up in efficiency levels? (iv) Were deeper cuts in public spending associated with stronger efficiency gains? (v) Has there been any relationship between changes in public sector efficiency and labor productivity across sub-regions? (vi) What are the determinants of sub-regional variation in public sector service efficiency?

A. Motivation

Studying how efficiency changes during large fiscal consolidation episodes is relevant since efficiency gains—along with secular trends induced by sector-specific reforms and technological improvements, for example—can help limit the adverse impact of spending cuts on outcomes. Yet, there is little evidence on how large “exogenous” fiscal consolidation episodes affect sub-regional public sector efficiency (or productivity):2 do they lead to unnecessary fat being trimmed or do existing institutional frameworks adjust to provide the same quality and quantity of services? In addition, little evidence is available documenting what happens to regional variation in the quantity or quality of public services. For example, would the less efficient sub-regions converge toward the others? Finally, the paper’s questions are also relevant because public sector efficiency is considered to be an important ingredient of economic productivity and performance more broadly (Evans and Rauch, 1999; Afonso et al. 2003; Kibblewhite, 2011).

The United Kingdom (UK) provides a useful case study since a sizable fiscal consolidation to reduce the build-up of public debt in response to the global financial crisis (GFC) has been undertaken. Despite the fact that the UK is separated into 12 regions (Wales, Scotland and Northern Ireland and the nine NUTS1 statistical regions of England3), the majority of public spending is centrally financed,4 unlike in many other countries where fiscal decentralization is more pronounced.56 This provides an “exogenous” shock which can be studied, since the extent of spending changes are not a function of levels or changes in spending efficiency in any one region or sub-region. Cognizant of the importance of public sector efficiency, a thorough review of government service productivity was initiated (Atkinson, 2005), with the Office of National Statistics (ONS) tasked with implementing the recommendations and providing estimates of multi-sector public sector productivity at the national level. The latest data indicate an improvement in overall public sector productivity post crisis (ONS, 2017).7

The novelty of this paper is a focus on sub-regional performance—relevant since discussions on fiscal decentralization (with the central authorities in London) are conducted at this level. Therefore, it combines official public spending data (at the English regional level8—i.e., the nine NUTS1 English regions barring Greater London, leaving eight regions9) and assembles sectoral output measures from various government departments (at the sub-regional level—i.e., 28 NUTS2 sub-regions or “counties”10, with Greater London sub-regions excluded) to estimate a sub-regional index of public efficiency. The approach used is related to Simar and Wilson (2007), Giordano and Tommasino (2013), and Giordano et al. (2015).

B. Stylized facts

Before estimating sub-regional public sector efficiency and addressing the main questions this paper seeks to answer, a few relevant stylized facts on public sector spending, three key sectoral outputs (education, health and economic services), and productivity for the UK and its English regions are shown next to set the stage for the empirical section.

Public and sub-regional spending in the UK are well below those of large EU and OECD economies (Figure 1). Government expenditure in the UK is below the European average and significantly below most comparator economies (Figure 1, left panel). This trend has become more pronounced and could continue in the future given the need for medium-term fiscal consolidation and the large current account deficit.11 The size of spending at the sub-regional level is also well below comparator economies (Figure 1, right panel).

Figure 1.
Figure 1.

Cross-country Developments in Public Spending

Citation: IMF Working Papers 2017, 036; 10.5089/9781475578966.001.A001

Sources: World Economic Outlook and Heseltine (2012)

Recent developments in sectoral public spending, achievement, and efficiency12 in England appear to be correlated (Figure 2). Given the large spending-led fiscal consolidation effort, it would be interesting to see how real education public spending per pupil, health and economic service expenditure per head (inputs) changed between the pre- and post-crisis periods, 2003–07 and 2010–14, respectively. It is also useful to see if there were any changes in achievement (outputs) associated with these spending changes, for example here in this paper, in high school education attainment of GCSE scores, life expectancy at the age of 65 years, and the number of private enterprises created themselves.13 These “inputs” and “outputs” have been widely used in the literature (e.g., Boyle, 2011; Giordano and Tommasino, 2013; reports of the UK’s National Audit Office), and Section IV.B examines a few alternative outputs.1415 Caveats in the choice of these outputs should be noted. First, cuts in primary education spending post-crisis would take some years to influence GCSE scores and more intermediate results (such as Key Stage 2 scores) are not examined due to data constraints at the sub-regional level. In addition, no distinction between private and state schools or pupils has been made given data constraints. Having said that, data from the Department of Education points to gradual improvement in national Key Stage 2 scores and regional pupil-to-teacher ratios in both primary and secondary education. Second, health spending not only aims to prolong life at birth or old age, but also to improve the quality of life—for example, by relieving chronic pain or addressing problems with mobility. Moreover, faster moving health outputs (e.g., hospital waiting lists, numbers of surgeries or hospital and clinic visits) would be preferable—but data limitations at the sub-regional level prevent such a choice. Also since life expectancy is a slow moving variable, studying changes over an even longer time horizon may be warranted—a task left for future research. Third, these and other quality adjustments, while important, are not studied at the sub-regional level, and thus are left for future research.16

Figure 2.
Figure 2.

Sectoral Inputs and Outputs and Efficiency

(Real £s per head or pupil, in percent)

Citation: IMF Working Papers 2017, 036; 10.5089/9781475578966.001.A001

Sources and notes: Author’s estimates based on official UK data at the NUTS1 level for inputs/spending and NUTS2 aggregated up to NUTS1 for outputs/achievement. Change refers to that between the average of the pre-crisis (2003–07) and the post-crisis (2010–14), in percent. Spending is in real £s per head or pupil.

Still it could be argued that for private sector productivity, for example, what matters in the end is the not the efficiency of public spending per se, but the actual quantity and quality of public services that is being provided (even if there is some waste). For example, if the decline in public spending on education was associated with a proportional decline in high school achievement, that may be damaging to the UK’s productivity regardless of what happened to public sector efficiency.

Scatter plots provide an intuitive first cut of the data at the 28 sub-regional and 8 regional levels between 2003 to 2014. The plots suggest that the post-crisis changes in spending per pupil and high school education attainment (proxied by the change in GSCE scores) are strongly and negatively correlated (Figure 2, first left panel), as are the changes in spending per pupil and estimated efficiency17 (Figure 2, first right panel), while the changes in health spending per head and health output (proxied by the change in life expectancy at 65 years of age) are positively correlated (Figure 2, second left panel), as are the changes in health spending per head and estimated efficiency (Figure 2, second right panel). The picture is less definitive for the changes in economic services (as proxied by the change in the number of private enterprises), although some positive correlation is apparent between inputs and outputs (Figure 2, third left panel) and negative between efficiency and inputs. Underestimated regional transportation spending may be behind these results (see annex).

Another first cut at the data suggests that while the large spending-led consolidation meant cuts across most spending categories in England, it was education spending per pupil that fell most dramatically, but achievement (at least in terms of the outputs used in this paper) was not adversely affected, rising instead across all sectors, including education (Figure 3 and annex Tables A.1 and A.2). The following findings, aggregated to the regional NUTS1 level, emerge:

  • Public health inputs or spending per head actually increased sharply across all English regions without exception post-crisis despite the large fiscal consolidation (Figure 3, yellow striped-bars),20 unlike that of education spending per pupil which declined sharply (Figure 3, orange striped-bars), particularly in the North. These spending cuts in education (which more than offset the health spending increases), were large with considerable variation across regions. Changes in public spending on economic services exhibit more sub-regional variation, with small cuts in some regions and small increases in others (Figure 3, green striped-bars).

  • There was no proportional decline in outputs commensurate with the proportional decline in spending, rather all outputs improved post crisis. In particular, life expectancy increased marginally (health output);21 GCSE achievement improved sharply22 (education output) most notably in the North and Midlands, and the number of enterprises expanded a little (economic services output) across regions, most notably in the East of England (Figure 3, yellow, orange and green solid-bars, respectively).23

Figure 3.
Figure 3.

Post Crisis Change in Regional Public Spending vs. Achievements

(in percent)

Citation: IMF Working Papers 2017, 036; 10.5089/9781475578966.001.A001

Sources and notes: Author’s estimates based on official UK data at the NUTS1 level for inputs/real spending and NUTS2 aggregated up to NUTS1 for outputs/achievement. Change between average pre (2003–07) and post (2010–14) crisis, in percent. Spending is in real £s per head or pupil.

These initial results suggest that despite large spending cuts, actual output (at least in terms of “quantities” measured here) has not suffered—rather it seems that excess fat in public spending has been trimmed.24 As mentioned, in the education sector in particular, Key Stage 2 results (tested at the end of primary school for pupils typically aged 11 years old) are unavailable at the sub-regional level but data on teacher-to-pupil ratios and class size (whether primary or high school) suggest gradual improvement post crisis.25 Clearly, factors other than changes in public spending could be driving these improvements—for example, technological improvements from computing, specific education and health sector reforms,26 and possibly incentives of sub-regional authorities to achieve greater “value for money” in the wake of fiscal decentralization, among others. And as mentioned, the “quality” of these outputs has not been measured and may not have a clear sectoral variation. Moreover, the long-term impact of the spending cuts may not be felt for years to come. Finally, it should be noted that during the GFC employment (in education and health specifically and in the overall economy more broadly) did not decline as sharply as in other OECD economies affected by the GFC, with the national unemployment rate in the UK remaining well below many of these economies. Hence, the cuts in spending do not appear to have affected at least the “quantity” of teachers, despite their real wages seeing modest declines.27 This along with lower pupil-to-teacher ratios (in primary and secondary) may well have contributed to some of the improved outputs, along with the incentives induced from sharp cuts in spending per pupil in the education sector, for example.

After 2008, overall economic output productivity growth in the UK declined much more than other advanced economies (Figure 4). Economic output productivity has been shown to be associated with public sector productivity or efficiency—also a proxy for the quality of governance (Giordano et al., 2015). Hence raising public sector productivity or efficiency might help boost overall productivity in the UK, which has seen the average annual growth of output per worker drop from almost 2 percent during 2000–08 to nearly zero during 2009 14 (Figure 4).28 As mentioned, the fact that the UK did not experience deep cuts in employment may have contributed to the cyclical weak labor productivity growth, on the one hand, and also in part supported the increase in outputs and efficiency in core sectors on the other.

Figure 4.
Figure 4.

cross-country Productivity

(average annual percent growth in output per hour)

Citation: IMF Working Papers 2017, 036; 10.5089/9781475578966.001.A001

Sources: Haver Analytics and IMF staff calculations.

The rest of this paper is organized as follows. Section II lays out the evidence-based empirical strategy, data and measurement issues and Section III reports the baseline results on sub-regional public sector efficiency. Section IV presents robustness checks and Section V draws conclusions and policy implications.

II. Empirical Strategy, Data and Measurement

Given the importance of public services to economic performance, this paper next combines official public spending data and sectoral output measures from various government departments to estimate an index of public productivity or efficiency at the sub-regional English level. The methodology used follows Simar and Wilson (2007), with the approach being related to Giordano and Tommasino (2013) and Giordano et al. (2015) who empirically estimate an index of public service efficiency across Italian provinces. The latter studies do not, however, differentiate between performance pre and post the GFC, for example, when austerity led to large spending cuts. This is one of the novelties of this paper. In particular, it constructs from scratch a sub-regional multi-sector public service (education, health and economic services) database, aggregating (weighted from the town or local level upward) to the NUTS2 sub-regional level, and then matching these with NUTS1 regional public spending data. It then uses a regression framework to empirically estimate public sector efficiency over two non-overlapping period averages: pre (2003–07) and post (2010–14) the GFC, using annual data. This allows for an analysis of the evolution and variation of public service delivery across England and its sub-regions (excluding London).29

A. Meatocidg public tectoc efficiency

Relation to the literature

Recent studies build on the microeconomic literature in measuring technical efficiency of a unit of production, by establishing the difference between an actual and a potential unit of output in relation to a unit of input—operational Pareto Optimality. Generalizing to all “input-output pairs” allows the construction of an efficient production frontier that connects or “envelopes” these combinations of input-output pairs, building on the idea of relative efficiency (Farrell, 1957) using non-parametric linear programming—the so-called Data Envelopment Analysis (DEA) developed by Charnes et al. (1978) and extended by Simar and Wilson (2007).30 DEA allows multiple input-output pairs to be considered at the same time without any assumption on data distribution. The relationship between spending (input) and performance (output) is thus benchmarked despite its drivers not having been directly observed.

Cross-country studies on public service efficiency or productivity using this approach include Afonso et al. (2003 and 2007), Gupta et al. (2007), Verhoeven et al. (2007), and Grigoli (2013). At the regional or local level, studies include Borge et al. (2008) for Norway, Revelli (2010) for the UK, and Giordano and Tommasino (2013) for Italy. However, no study has yet examined how public sector efficiency has changed sub-regionally post the GFC following a large fiscal consolidation episode and across most spending categories or sectors.31 This is one of the contributions of this paper.

Methodology

A sub-regional index of public sector efficiency is constructed using DEA regression analysis based on total (central, regional, county, local) spending data on the three key public services across the English regions: education, health, economic affairs (including transport and housing).32 The regional spending data is complemented by sub-regional “control” variables, e.g., changes in private spending on the examined public services, income per capita, population density and its age-profile, and capital stocks, among others.

Non-parametric treatment of the efficiency frontier does not assume a particular functional form, but relies instead on the general regularity properties, such as monotonicity, convexity, and homogeneity. The DEA is based on a linear programming algorithm,33 constructing an efficiency frontier from the data in all “single decision units”—here being a sub-region or county, such as the Greater Manchester Combined Authority. A DEA model can be subdivided into an input-oriented model (which minimizes inputs and controls while satisfying at least a given level of output) or an output-oriented model (which maximizes outputs without requiring more of any of the observed input or control values). The latter is chosen in this paper, as these models are the most frequently used because the quantity and quality of inputs (public spending and other controls defined here) are assumed to be fixed exogenously, hence the sub-regional authorities cannot influence these, at least not in the short-run.34

DEA models can also be subdivided in terms of returns to scale by adding weight constraints. Constant returns to scale are chosen here as the baseline, as there is no conclusive evidence to suggest that the production of public services (whether in health, education or economic services) varies in technology across English regions or sub-regions outside Greater London—particularly during the past four decades since the creation of the National Health Service and the state school system, unlike firms. However, variable returns to scale technologies (i.e., increasing or decreasing) were also estimated but do not suggest a material change to the results.35 A specific sub-region is called efficient when the DEA score equals to one and slack is zero. Inefficiency can be seen in terms of how much the inputs and control variables must contract along a ray from the origin until it crosses the frontier (Ji and Lee, 2010).

Spending on the three categories of education, health, and economic affairs represents over 50 percent of total public spending over the past decade, with all three having been shown in the literature to influence economic prospects over time (Afonso et al. 2003), and the remainder largely being spending on pensions and social protection.3637 The assumption is that this spending does not vary within each region, only across regions (Giordano and Tommasino 2013, Giordano et al. 2015).38 By and large, all regions experienced public spending cuts post crisis, with the exception of health—where spending per head rose across regions with limited (NUTS1) regional variation (Figure 3 and Table A.1). On the other hand, spending cuts in education (which offset the health spending increases), were large with considerable variation across regions. For example, the North experienced cuts per pupil between three to 8½ times more than the South (Figure 3 and Table A.1).

Performance outputs and other control variables vary within regions—in other words they are available and have been collected from various government departments at the sub-regional (NUTS2) level (see the data appendix). Two cross-sections are examined to compare the pre-and post-crisis average performance (2003–07 vs. 2010–14) given data availability.39 This allows for the coefficients to vary between the pre- and post-crisis periods, capturing the dynamic changes. Outputs are those of the 28 English counties.4041

B. Estimation

The DEA regression is estimated, for each of the two non-overlapping period averages, pre-and post-crisis. The production process is constrained by the production set:

Ψ={(x,y)R+N+M|xcan produce y}(1)

where x represents a vector of N inputs (public spending by sector and controls as specified below for each sector) and y the vector of M outputs by sector (as shown in more detail below). Three separate production processes are estimated for each sector. Each production frontier is the boundary of Ψ. In the interior of the Ψ there are units that are technically inefficient while technically efficient ones operate on the boundary of Ψ, i.e., the technology frontier. If the production set is described by its sections, then the output requirement set is described for all xR+N:

Y(x)={yR+M|(x,y)Ψ}(2)

The output-oriented efficiency boundary ∂Y(x) is defined for a given xR+N as:

Y(x)={y|yY(x),λyY(x),λ>1}(3)

and the output measure of efficiency for a production unit located at (x,y)R+N+M(x,y)) is:

λ(x,y)=sup{λ|(x,λy)Ψ}(4)

Because the production function set Ψ is unobserved, in practice efficiency scores λ(x,y) are obtained by DEA estimators, for example, for output orientation with constant returns to scale, and the solution is found through the linear program:

λ^CRS(x,y)=sup{λ|x,λyΣi=1nγiyixΣi=1nγixi for (γ1,γn)}(5)

such that: γi ≥ 0, i = 1,…, n

The three sectors are:

  • Education. Input: Real public expenditure on education per pupil; Other inputs or control variables: Private spending on education and education attainment by income level per head; Output: High school (GCSE) achievement.44

  • Health. Input: Real public expenditure on health per head; Other inputs or control variables: Adjusted for population’s age structure (i.e., ratio of population over 65);45 and the prevalence of obesity. Output: Life expectancy at the age of 65 years.47

  • Economy. Input: Real public expenditure on economic services, including transport and housing, normalized by lagged population size; Other inputs or control variables: Lagged stock of capital; Output: Number of active enterprises.48

III. Baseline Findings

This section examines the following questions: (i) Has sub-regional public sector efficiency improved or weakened in England during the fiscal consolidation of 2010–14? (ii) What has been the pattern across different sectors and sub-regions? (iii) Have sub-regions with lower initial levels of efficiency experienced stronger gains, implying some catch up in efficiency levels? (iv) Were deeper cuts in public spending associated with stronger efficiency gains? (v) Has there been any relationship between changes in public sector efficiency and labor productivity across sub-regions?

Sub-regional efficiency scores reassuringly show stability over the estimation sub-sample periods (Table 1). The estimated efficiency scores, λ^CRS(x,y), from the DEA regression (equation 5) are presented for each sector and combined into a simple average—a weighted average produces similar results (see Section IV.A). Higher values imply higher efficiency and the score of one implies a county that was most efficient.49 Despite large public spending cuts, overall efficiency improved post crisis (Table 1 and Figure 5). Efficiency improved most notably in the education sector, which saw the deepest cuts, followed by health (which instead saw spending increases). However, the efficiency of economic services deteriorated slightly. In terms of sub-regions, at one end, Tees Valley and Durham (UKC1) improved its efficiency post crisis, but at the other end, Devon, (UKK4), saw a deterioration (including but not limited to the reduction in public spending). The lower quartile of efficiency, however, remains a northern-county phenomenon. Determining whether the post crisis improvements in efficiency scores are statistically significant is not straightforward, however. While bootstrapping and Bayesian methods have been used to determine the statistical significance of the DEA results, none of these methodologies can estimate, with a specified probability, the confidence interval for the true efficiency scores.50

Figure 5.
Figure 5.

Convergence of Weaker NUTS2 Sob-regiont

(DEA efficiency level during pre-crisis vs. percent change post-crisis)

Citation: IMF Working Papers 2017, 036; 10.5089/9781475578966.001.A001

Table 1.

Public Sector Efficiency Scores Computed by Data Envelopment Analysis for English Counties 1

article image

Constant returns to scale.

Simple average.

Sub-regions with the weakest pre-crisis levels in public sector efficiency converged the most (Figure 5 and Table 2). Worse off sub-regions achieved the largest improvements, as evidenced from a regression of the efficiency gains on the initial level of efficiency.51 Indeed, sub-regions with lower initial levels of efficiency experienced the strongest gains in each of the education, health, economic services sectors and the overall average (Table 2, columns 1, 2, 3, and 4, respectively).52

Table 2.

Did sub-regions with weaker initial efficiency converge more?

Bivariate regression results

article image
Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

Change between pre and post crisis, 2003–07 and 2010–14, respectively.

Initial efficiency levels refer to 2003–07 averages.

What explains this strong convergence in public sector efficiency? Many factors could be at play, including fundamental covariates (e.g., sub-regional GDP per capita, other domestic sector-specific reforms, and possibly external variables, e.g. funding) and perhaps changing incentives among sub-regional authorities in the wake of spending devolution and fiscal consolidation.53 This issue is partly examined in Section IV.

Deeper education spending cuts are associated with large public sector efficiency gains in that sector (Table 3). When regressing the percentage change in efficiency scores, on the percentage change in public spending, for each sector as well as the simple overall average of these three sectors, deeper spending cuts only in the education sector are found to have led to larger sub-regional efficiency gains (Table 3, column 1). This suggests that the larger cuts in education may well have forced institutions across sub-regions to adapt and trim their activities with lower returns.54 However, this is not the case for economic services and the simple average of the three sectors—which display the expected sign but their coefficients are statistically insignificant (Table 3, columns 3–4). The increase in spending in the health sector actually led to efficiency increases—although the coefficient is also insignificant (Table 3, column 2). This suggests that other factors have raised efficiency in the health sector (other than public spending), such as technological improvements, skill enhancements of health professionals, and other sector-specific reforms.55

Table 3.

Did deeper spending cuts lead to larger efficiency gains?1

Bivariate regression results

article image
Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

Change between pre and post crisis, 2003–07 and 2010–14, respectively.

Sub-regional changes in public sector efficiency are associated with changes in sub-regional productivity. Despite considerable variation, correlation between public sector efficiency and productivity per worker is evident from a visual inspection (Figure 6). In particular, sub-regions that have improved their level of public sector efficiency or productivity in the postcrisis period also tend to have higher labor productivity growth (Merseyside, Greater Manchester, Northumberland and Tyne and Wear, West and South Yorkshire, Derbyshire and Nottinghamshire and the West Midlands), and vice versa (North Yorkshire, Lancashire, Cornwall and Devon, Bedfordshire and Hertfordshire, Leicestershire, Rutland and Northamptonshire, Herefordshire, Worcestershire and Warwickshire). A regression of the change in sub-regional efficiency on that of labor productivity growth suggests that the coefficient is statistically significant (at the 5 percent level). However, the association between the change in public sector efficiency and that of productivity does not imply causality, as there are clearly other factors driving each despite some interrelation. Nevertheless, the positive correlation suggests that delving into this matter (e.g., using micro data) could be a fruitful direction of future research.

Figure 6.
Figure 6.

Post Crisis Change in Public Sector Efficiency and Productivity

(NUTS2 sub-regions)

Citation: IMF Working Papers 2017, 036; 10.5089/9781475578966.001.A001

Notes and sources: Post crisis change in sub-regional public sector efficiency is as estimated above. Labor productivity is measured as the change in real output per worker between 2003–07 and 2010–14 (ONS, 2016).

Sectoral sub-regional disparities in the efficiency of public services narrowed post crisis (Figure 7). Variation appears widest in economic services efficiency—how spending per head (input) and capital stock (control variable) is translated into the creation of private enterprises (output). This variation persisted post crisis with very little change—likely the result of limited infrastructural spending in the post crisis period. However, sub-regional variation in the efficiency of delivering educational services (GCSE scores, in particular) was less pronounced and narrowed markedly post crisis (by 44 percent). This finding of narrower variation runs counter to the finding of Whitty (2000), who found evidence of increased polarization (variation) in examination results a decade earlier. Variation in health services was more moderate than that in economic services but still larger than in educational services, and also narrowed post crisis (by 11 percent). Once again, other factors (mentioned above) beyond the change in public spending could have contributed to the reported narrower variation findings here. Section IV.C (Table 8) picks up the issue of the drivers of sub-regional variation.

Figure 7.
Figure 7.

Ditparitiet in Sub-Regional Public Sector Efficiency

Citation: IMF Working Papers 2017, 036; 10.5089/9781475578966.001.A001

Pre-and post-crisis refer to the 2003–07 and 2010–14 average, respectively.
Table 4.

Average weights of main spending categories 1

(£ ’000)

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Average weights during 2003–14 of NUTS1 spending for England excluding London.

Table 5.

Robustness—Weighted Public Sector Efficiency Scores Computed by Data Envelopment Analysis 1

article image

Constant returns to scale.

Weighted average.

Table 6.

Robustness: Did sub-regions with weaker initial efficiency converge more?

Bivariate regression results

article image
Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

Change between weighted efficiency index pre and post crisis, 2003–07 and 2010–14, respectively. Initial efficiency levels refer to 2003–07 averages.

Education spending is lagged one year (two lags were insignificant), and NUTS2 teacher-pupil ratios are included as a control variable. On the latter, data is only available since 2006.

Private health spending is added as a control from household surveys, as is a mother’s smoking status at time of delivery (data is only available since 2006), and life expectancy at birth (HALE) is the output.

Economic service spending is lagged two years (one year lags were insignificant) and the output here is labor producivity.

Table 7.

Robustness: Did deeper spending cuts lead to larger efficiency gains?1

Bivariate regression results

article image
Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

Change between weighted efficiency index pre and post crisis, 2003–07 and 2010–14, respectively.

Education spending is lagged one year (two lags were insignificant), and NUTS2 teacher-pupil ratios are included as a control variable. On the latter, data is only available since 2006.

Private health spending is added as a control from household surveys, as is a mother’s smoking status at time of delivery (data is only available since 2006), and life expectancy at birth (HALE) is the output.

Economic service spending is lagged two years (one year lags were insignificant) and the output here is labor producivity.

Table 8.

Robustness: Determinants of sub-regional (NUTS2) spending efficiency scores?1

Multivariate truncated regression results

article image
Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

Two sets of efficiency scores per sub-region for each of the pre- and post-crisis periods and each regressor.

IV. Robustness Checks

Three sets of robustness checks are studied in this section. First, the estimated efficiency scores are weighted by their corresponding sectoral shares of public spending. Second, alternative outputs and control variables, among others, are considered, depending on data availability. Third, as a complement to the DEA, a stochastic frontier analysis is undertaken to address some reported endogeneity difficulties when measuring efficiency in the education sector. This third check allows one to answer the following question: What are the drivers of sub-regional efficiency variation post-crisis?

A. Weighted average DEA tcoret

The DEA estimated efficiency scores are weighted by their corresponding sectoral shares of public spending at the NUTS1 level, in case particular NUTS1 regions’ spending is concentrated in one sector more than others, so as not to under or overestimate the combined average—instead of the simple average of the three sectors shown in Table 1. The weights used are the average shares of the sectoral spending for the full sample (Table 4) and result in a similar ranking of sub-region efficiency (Table 5) with all baseline results reported in Section III holding.

B. Alternative inputt, outputt and control variablet

Alternative or additional specifications of outputs and control variables are considered next, depending on data availability, along with the issue of lags in public spending.

In the education sector, pupil to teacher ratios (or class size when unavailable) are used as an additional control variable (given mentioned problems associated with GCSE score inflation and other factors that could have contributed to increased education outputs post crisis), while education spending is lagged for one year due to relatively strong contemporaneous effects of public spending on achievement in the state school system, and in poorer sub-regions in particular (Jackson et al. 2016). While previously mentioned caveats still hold, including the problem of the absence of primary schooling outputs, the education sector coefficients using higher order lags of public spending in Tables 6 and 7 were insignificant albeit similar in magnitude and sign.56 The resultant DEA scores for the education sector do not vary significantly from those shown in the baseline as a result of these robustness checks (Table 5).

For the health sector, instead of (the tougher) life expectancy at the age of 65 years, life expectancy at birth (HALE) is the main output, health spending is lagged two years to reflect some non-contemporaneous dynamics, and two additional control variables (or inputs) are added: private spending on health from household surveys, and the smoking status at the time of birth delivery.5758 Despite these new variables, the output still suffers from the caveats noted earlier and thus the results should still be interpreted with some caution. The resultant DEA efficiency scores do not alter in terms of the sub-regional ranking for the health sector nor do the changes post crisis (Table 5).

As an alternative to the number of enterprises created, labor productivity is used for the output for public economic tervice spending,59 with spending itself lagged two years. Here there were some changes in the ranking order of sub-regions, unlike other checks, however the post crisis changes remain in the same order or magnitude as those reported in the baseline (Table 5).

Using these alternative inputs, outputs and controls presented in this section, the baseline result that sub-regions with the weakest levels of public sector efficiency converged the most (when re-estimating the regression of the efficiency gains post crisis on the initial level pre crisis, for each sector as well as the weighted overall average of the three sectors) still holds, with each co-efficient displaying the same sign, similar magnitudes, and with slightly more statistical significance and larger R-square (Table 6).60

Turning to the robustness of the baseline results shown earlier in terms of whether deeper spending cuts have led to larger efficiency gains (when re-estimating the regression of the percentage change in efficiency on the percentage change in public spending, for each sector as well as the weighted overall average of these three sectors), the results suggest that not only are the coefficients slightly larger, but now they also gain in statistical significance and have larger R-square (Table 7).61 Despite these results, the problem of the endogeneity of public spending remains. Hence the next section attempts to address the issue through a two-stage regression framework.

C. Stochattic frontier analytit

First-stage analysis

One of the limitations of the DEA efficiency estimation is its inability to fully control for heterogeneity, for example, in terms of differences in levels of development or income (Green, 2004; Grigoli, 2014). While control variables were introduced to address sub-regional differences across England in the baseline DEA estimation (Section II and III) for robustness, a parametric stochastic frontier analysis is examined next.

Parametric techniques, including stochastic frontier analysis (SFA), are essentially econometric models requiring assumptions regarding the functional form of the production frontier. Advantages of the parametric approach relative to the non-parametric ones (as in the DEA) include controlling for a larger number of variables (that can influence each public sector output, in this case) and more limited sensitivity to outliers. Both are particularly relevant for cross-country studies, e.g., when studying differences among a heterogeneous group—such as for developing versus advanced economies. But they also are relevant to “within country” analyses—for example, here the issue of outliers was one of the reasons behind excluding Greater London in the analysis.

The SFA approach is similar to the DEA in that a technological frontier envelops all input-output pairs. In addition, from the statistical point of view, the regression model is characterized by a composite error term in which the idiosyncratic (normally distributed) disturbance capturing measurement error is included together with a one-sided disturbance which represents inefficiency.

Two SFA cross-sections are estimated using a Cobb-Douglas production function for each sector and for each of the pre- and post-crisis sub-sample averages, which are identical to those in the baseline DEA efficiency estimation. The cross-sections are estimated by maximum likelihood, with the difference from the DEA being that the estimation is carried out in two separate steps. The first step is a regression of each sector’s outputs on its lagged inputs to estimate SFA efficiency scores.62 The inputs and outputs are precisely those introduced in this section—i.e., the “alternative inputs and outputs” described on pages 2325. As can be seen from the results of the SFA cross-sectional estimation (Figure 8),63 the sub-regional efficiency scores are smaller in size but their ranking and magnitude of change post crisis (or convergence) are highly and significantly correlated to those estimated in the baseline DEA (whether for the simple or weighted average scores), despite the fact that no control variables have been included in the SFA estimation yet—although now there is more variation in the sub-regional scores (i.e., distance from the frontier).

Figure 8.
Figure 8.

Robuttnett: Convergence of Weaker NUTS2 Sub-regiont

(Estimated SFA weighted-average efficiency pre-crisis levels vs. percent change post crisis)

Citation: IMF Working Papers 2017, 036; 10.5089/9781475578966.001.A001

Second-stage analysis

The second step estimates the determinants or covariates of sub-regional public sector spending efficiency in the three sectors of education, health and economic services and the weighted overall sectoral average. This separate second step partly addresses the problem of public spending endogeneity, particularly if using lags in public spending among other instruments. The SFA efficiency scores can be thought of as a rescaled measure of how much GSCE achievement, for example, a sub-region can achieve at the spending levels pre- or post-crisis if it were as efficient as the most efficient sub-region in England during the same periods.

A multivariate truncated regression with fixed effects is run to identify the factors that account for the sub-regional variation in the efficiency scores given that the SFA efficiency scores are bounded (between zero and one).64 For this truncated regression, all estimated SFA scores during the pre- and post-crisis periods (28 sub-regions and their average x 2-period averages, and hence 58 observations) are included on the left hand side, and all control variables along with a fixed effect per sub-region per period average are on the right hand side. The results suggest (Table 8):

  • Income per capita. This captures the effect of disposable income on high-school education achievement (GCSEs) and life expectancy at birth. The coefficient is positive but small as expected, and is statistically significant only in the case of education. The insignificant coefficient on health suggests that the NHS has contributed to improved sub-regional health outputs regardless of private disposable incomes.

  • Private spending on education or health. This captures the effect of households’ ability to complement public spending in achieving better high-school education and health outputs. The coefficient is positive and large in the case of education but small and statistically insignificant in the case of health. The former suggests that private spending on students during the GCSE year (and one-year prior) is important and contributed significantly to sub-regional variation in attainment. The latter suggests that while private spending on health displays the right sign, it is insignificant in terms of explaining sub-regional variation.

  • Education attainment by income level. Higher education attainment of parents or the sub-regional population is likely to imply better GSCE achievement of their children and similarly better health and economic service outputs. All coefficients are positive, large and statistically significant, suggesting that education attainment does indeed explain a large fraction of the sub-regional variation in efficiency scores.

  • Pupil-to-teacher ratio. In many OECD countries, a lower number of pupils per teacher (or smaller class-size) is commonly associated with more efficient public spending on high school education (Grigoli, 2014; Jackson, 2016). The coefficient is indeed positive for secondary education albeit small and is statistically significant.

  • Population dentity. The quantity and quality of public services provided in education, health, and economic affairs is usually easier to carry out in areas that are urban or more densely populated since commuting distances are shorter and the diffusion or transfer of knowledge and innovation is faster and competition brisker than in rural areas. As expected the coefficients are positive, large and statistically significant for all sectors implying that population density (a proxy for “connectivity” perhaps) is also an important factor explaining the variation in spending efficiency across all sectors.

  • Smoking status at time of delivery. For the health sector alone, the smoking status of the family at the time of the delivery of a birth could be associated with life expectancy at that time. The coefficient is positive but small and insignificant, implying that smoking is not an important factor explaining the variation in sub-regional public health spending efficiency.

  • Prevalence of obesity. Once again, for the health sector alone, the prevalence of obesity in mothers at the time of the delivery of a birth could be associated with the life expectancy of the child born at that time. The coefficient is positive but small and insignificant, implying that obesity has not been an important factor explaining the variation in public health spending efficiency.

  • Capital stock. For economic services and the weighted average alone, larger capital stocks could be associated with higher economic service efficiency scores since higher capital output ratios augment and influence labor productivity as they contribute not only to easing transportation and housing bottlenecks, but also bring in more jobs and opportunities that would increase efficiency diffusion. However, the coefficient does not display the expected sign, is small and only statistically significant in the case of the weighted average. This could reflect measurement error in the input, which is likely to be underestimated (see the data appendix).

  • Number of active enterprises. For economic services alone, more abundant firms could be associated with higher public economic service efficiency scores; since the more abundant the firms, the more experienced and sophisticated are the public service providers in terms of the delivery of auxiliary business services offered. As expected, the coefficient is positive, large and statistically significant, suggesting that the creation of firms is an important determinant of sub-regional efficiency variation.

Overall, it appears that the most important determinant of sub-regional variation in public sector service productivity or efficiency since 2003 is education attainment, followed by population density (a proxy for urbanization or connectivity), and then private spending and class size on education.

V. Conclusions and Policy Implications

How public sector efficiency or productivity changes during large fiscal consolidation episodes is relevant since efficiency gains can help limit to some extent, along with other secular trends, the adverse impact of spending cuts on outputs. Yet, there is little evidence on how large “exogenous” fiscal consolidation episodes affect sub-regional public sector efficiency and its variation. Despite data limitations at the sub-regional level, and therefore several caveats to the paper’s findings, it offers a first stab at filling the gap in terms of what has happened to sub-regional multi-sector public sector productivity post crisis. Its findings could help inform multi-sector spending reforms in the wake of fiscal spending devolution in England at this sub-regional (county or Combined Authority) level. The paper first found that the actual “quantity” of public services provided post crisis broadly has not declined with the proportional decline in education spending per pupil or spending in economic services, for example. Moreover, health spending per head and its associated outputs have increased post crisis.

While these results are encouraging, other factors could also be at play—such as technological improvements, sector reforms, stable employment and incentives of sub-regional authorities in the wake of fiscal devolution. In addition, the choice of outputs in both the education and health sectors does suffer from some shortcomings—therefore making data available on more complete (e.g., Key Stage 2 examination results) and faster moving (e.g., hospital waiting lists) outputs at the sub-regional level would aid further study. In addition, despite these assuring and somewhat unexpected findings, employers have complained about skill deficiencies among the young and those with relatively low education attainment. There is also a high proportion of negative growth firms and room to improve managerial capabilities in the economy (Heseltine 2012). Changes to the “quality of outputs” has also not been measured. Finally, the long-term impact of some of the spending cuts may not be felt for years to come, especially concerning health outputs (as this is a slow moving variable) and the impact of cuts in primary education on high school achievement and education attainment in England more broadly. Further analysis using micro data at the sub-regional level, for example, could provide a fruitful avenue of future research and complement the research undertaken in this paper.

Nevertheless, the paper sought to answer the central question of how much has public sector service efficiency or productivity in sub-regional England and its variation changed since the crisis following the large fiscal consolidation in an evidence-based (empirical) setting. After constructing a sub-regional database that combines official public spending variables matched with several leading multi-sectoral output measures used in the literature from various government departments, an index of public efficiency at the sub-regional English level is estimated.

Through a regression framework, the main empirical findings are: (i) despite large public spending cuts, most notably in the education sector, overall efficiency improved post crisis, with larger cuts yielding the highest efficiency improvements across sub-regions most notably in the education sector, although the lower quartile remains a northern-county phenomenon; (ii) notwithstanding lower initial efficiency levels, these northern counties made the largest efficiency gains post crisis, thus contributing to a narrowing in regional disparities across England; (iii) and while sectoral disparities in the efficiency of delivering public services in economic affairs were widest and remained broadly unchanged post-crisis, those for education (following spending cuts, among other factors) and health (following spending increases, among other factors) have narrowed markedly.

These results could help inform policy makers when designing fiscal spending reforms, including decentralization, across the English sub-regions. In particular, spending powers to those sub-regions that delivered the largest improvements in efficiency could be devolved first, and if the improvements persist, consideration could be given to granting revenue generation powers next, for example. However, for those which saw a deterioration, benchmarking as an incentive to improve future performance could be warranted. Robustness checks revealed that the drivers behind sub-regional variation in public sector efficiency levels since 2003 were fundamental factors such as education attainment of households, population density, private spending on high school education and class size. These could be a sign that reforms to increase sub-regional connectivity (and reduce the costs of transportation) while increasing education attainment and reforms in some sub-regions are worthwhile (for many sound economic reasons, not least) because they would help narrow sub-regional disparities in public sector productivity further.

Finally, the paper found that post-crisis sub-regional changes in public sector efficiency are associated with changes in post-crisis sub-regional labor productivity. However, given the finding that public sector efficiency has improved, this suggests that either there are lags between the two variables, with productivity possibly improving with a delay post improvement in public sector efficiency, and that there are other factors beyond the change in public sector efficiency driving the UK’s weak “productivity puzzle”. Exploring these factors and continuing to delve more deeply into the UK’s productivity puzzle at the sub-regional level would be an important avenue for future research.

Referencet

  • Afonso, A., V. Tanzi, L. Schuknecht, 2003. “Public Sector Efficiency: An International Comparison, ECB Working Paper No. 242.

  • Afonso, A., V. Tanzi, L. Schuknecht and N. Veldhuis, 2007. “Public Sector Efficiency: An International Comparison, Fraser Alert, The Fraser Institute.

    • Search Google Scholar
    • Export Citation
  • Atkinson, T., 2005. “Atkinson Review: Final Report—Measurement of Government Outputs and Productivity for the National Accounts”, Palgrave MacMillan.

    • Search Google Scholar
    • Export Citation
  • Belotti, F., S. Daidone, and G. Ilardi, 2012. “Stochastic frontier analysis using Stata,Centre for Economic and International Studies, Research Paper Series, Vol. 10, Issue 12, No. 251 (sept. 2012).

    • Search Google Scholar
    • Export Citation
  • Borge, L-E., T. Falch, P. Tovmo, 2008. “Public Sector Efficiency: The Roles of the Political and Budgetary Institutions, fiscal capacity, and Democratic Participation”, Public Choice (2008) 136: 475495.

    • Search Google Scholar
    • Export Citation
  • Boyle, S., 2011. “Health Systems in Transition”, United Kingdom (England).

  • Charron, N., 2012. “From Aland to Ankara: European Quality of Government Index”, University of Gothenburg Working Papers 2013:11

  • Evans, P., and J.E. Rauch, 1999. “Bureaucracy and Growth: A cross-National Analysis of the Effects of “Weberian” State Structures on Economic Growth,American Sociological Review, Vol. 64(5), pp. 748765.

    • Search Google Scholar
    • Export Citation
  • Farrell, M.J., 1957. “The Measurement of Productive Efficiency,Journal of the Royal Statistical Society Vol. 120, pp. 253281.

  • Fried, H., K. Lovell, and S. Schmidt, 2008. “The Measurement of Productive Efficiency and Productivity growth (Oxford: Oxford University press).

    • Search Google Scholar
    • Export Citation
  • Giordano, R., and P. Tommasino, 2013. “Public-Sector Efficiency and Political Culture,FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tubingen, vol. 69(3), pages 289316, September.

    • Search Google Scholar
    • Export Citation
  • Giordano, R., S. Landau, P. Tommasino, P. Topalova, 2015. “Does Public Spending Inefficiency Constrain Firm Productivity: Evidence from Italian Provinces”. IMF Working Paper 15/168.

    • Search Google Scholar
    • Export Citation
  • Greene, W., 2004. “Distinguishing between Heterogeneity and Inefficiency: Stochastic Frontier Analysis of the World Health Organization’s Panel Data on Health Care Systems,Health Economics, Vol. 13, pp. 95980.

    • Search Google Scholar
    • Export Citation
  • Green, W., 2008. The Measurement of Efficiency, chap. The Economic Approach to Efficiency Analysis. Oxford University Press.

  • Grigoli, F. and J. Kapsoli, 2013. “Waste Note, Want Not: The Efficiency of Health Expenditure in Emerging and Developing Economies.IMF Working paper 13/187.

    • Search Google Scholar
    • Export Citation
  • Grigoli, F., 2014. “A Hybrid Approach to Estimating the Efficiency of Public Spending on Education in emerging and developing Economies.IMF Working paper 14/19.

    • Search Google Scholar
    • Export Citation
  • Heseltine, M., 2012. “No Stone Unturned—In Pursuit of Growth.

  • Hughes, A., 2008. “Entrepreneurship and Innovation Policy: Retrospect and Prospect.Political Quarterly 79, 2008.

  • International Monetary Fund, 2016. The UK Article IV Consultation and Selected Issues Paper, 2016.

  • Jackson, C. K., R. C. Johnson, and C. Persico, 2016. “The Effects of School Spending on Educational and Economic Outcomes: Evidence from School Finance Reforms”, Quarterly Journal of Economics (2016), 157218.

    • Search Google Scholar
    • Export Citation
  • Ji, Y-b, and C. Lee, 2010. “Data Envelopment Analysis”, The Stata Journal (2010) 10, Number 2, pp. 267280.

  • Kibblewhite, A., 2011. “Role of Public Sector Performance in Economic Growth,Speech delivered by Mr. Kibblewhite, Deputy Chief Executive, The Treasury, at the Institute of Policy Studies, Wellington, 1 April 2011

    • Search Google Scholar
    • Export Citation
  • Lloyd, T., 2015. “Historical trends in the UK: Funding Overview”, The Health Foundation.

  • Machin, S., 2015. “Real Wage Trends”, Understanding the Great Recession: From Micro to Macro Conference, Bank of England, Sept. 2324 2015.

    • Search Google Scholar
    • Export Citation
  • Oats, W., 1972. Fiscal Federalism (New York: Harcourt Brace Jovanovich).

  • Office of National Statistics, 2017. “Public service productivity estimates: total public service, UK:2104.Office of National Statistics.

    • Search Google Scholar
    • Export Citation
  • Phillips, D., 2015. “Local Government and the Nations: A Devolution Revolution?Institute of Fiscal Studies.

  • Ray, S., 2004. “Data Envelopment Analysis: Theory and Techniques for Economies and Operations Research (Cambridge: Cambridge University Press).

    • Search Google Scholar
    • Export Citation
  • Revelli, F., 2010. “Spend More, Get More? Any Inquiry into English Local Government Performance”. Oxford Economic Papers 62(2010), 185207

    • Search Google Scholar
    • Export Citation
  • Simar, L., and P. W. Wilson, 2007. “Estimation and Inference in Two-stage, Non-parametric Models of Production Processes. Journal of Econometrics, 13664.

    • Search Google Scholar
    • Export Citation
  • Travers, T., 2015. “A Hyper-Centralized Anomaly: why the UK Must Embrace Tax Devolution”, in Tax for Our Times: How the Left Can Reinvent Taxation, edited by D-R Smith, Fabian Ideas 640.

    • Search Google Scholar
    • Export Citation
  • Verhoeven, M., V. Gunnarsson, and S. Carcillo, 2007. “Education and Health in G7 Countries: Achieving Better Outcomes with Less Spending,IMF Working paper 07/263.

    • Search Google Scholar
    • Export Citation
  • Whitty, G., 2000. “Education reform and education politics in England: A Sociological Analysis”. Institute of Education.

  • Witty, A., 2013. “Encouraging a British Invention Revolution: Review of Universities and Growth”. Final report and Recommendations.

Appendix

The NUTS2 statistical classification of the United Kingdom, including all its regions:

Data and sources

A schematic of the data used in this paper, brief definitions and sources it as follows:

article image

Real public spending is available by NUTS1 level based on devolved administration spending and the subset of departmental spending that can be identified as benefiting the population of individual regions, combined with the known spending of local government (accounted for by the Department for Communities and Local Government). The data cover central government, local government and public corporations, with some caveats—see next. Source: Her Majesty’s Treasury, “Public Expenditure by Country, Region and Function” Chapter 9. November 2015.

Real sectoral NUTS1 spending on economic affairs, transport and housing. Spending on economic affairs and housing is the sum of the following HMT budget chapters: Public and common services; Public order and safety; Economic affairs including enterprise and economic development, science and technology, employment policies, agriculture, fisheries and forestry, transport; Environment protection; and Recreation, culture and religion. It should be noted that much of rail and air transport spending cannot be apportioned on a regional basis and is thus likely to be an underestimate. Source: Her Majesty’s Treasury, “Public Expenditure by Country, Region and Function” Chapter 9. November 2015.

Real NUTS1 private spending on public services (e.g., education and health) is from the household survey. Source: Office of National Statistics.

Real gross disposable income is available by NUTS1 and 2. Source: Office of National Statistics.

Number of pupils is available at the school level and aggregated up to NUTS2 level using the ONS Geography and GIS & Mapping’s keys for local administrative units. Source: Department of Education.

Education achievement is GCSE of 5 or more A*-C grades at GCSE or equivalent, including English and Maths, at Key Stage 4 as a percentage of the number of pupils at the end of KS4. It should be noted that since 2012/13 evidence points to score inflation in part attributed to increases in the number of non-GCSE results and hence do not reflect a change in education output. However, the historically consistent Level 2 attainment is used here. Scores are weighted by the number of pupils for aggregation to the NUTS 2 level using the ONS geography and GIS & Mapping Unit keys. Source: Department for Education.

Pupil teacher ratio for secondary is available at the NUTS1 level and for years where the data is missing, the series is spliced with the change in secondary clatt tize from the same source but at the local school level upward to aggregate up to NUTS1 level using the ONS geography and GIS & Mapping Unit keys. Source: Department of Education.

Population at NUTS2: Total resident population (midyear population estimates). The estimated resident population of an area includes all people who usually live there, whatever their nationality. Members of UK and non-UK armed forces stationed in the UK are included and UK forces stationed outside the UK are excluded. Students are taken to be resident at their term time address. The data reflect the new methodology used to calculate migration. Source: Office of National Statistics.

Life expectancy at birth and the age of 65 or at birth. Data is derived from the NUTS2 Annual

Population Survey (APS). Source: Office for National Statistics.

Population age structure (i.e., ratio of population over 65) and dentity. Data is from the NUTS2

Annual Population Survey (APS) and EuroStat Population Database. Source: Office for National Statistics and http://ec.europa.eu/eurostat/statistics-explained/index.php/Population_statistics_at_regional_level

Number of active enterprises: Active enterprises at the NUTS2 level are defined as those that had either turnover or employment at any time during the reference period. This is a count of active enterprises in the area. This indicator is a refinement of the indicator covering the number of VAT-registered businesses at the start of the year: for instance, it recognizes business activity occurring at any point in the year and it picks up PAYE-registered business as well as VAT-registered businesses. As a result of this being a more comprehensive measure, the figures are slightly higher than for the VAT-registered businesses measure. Source: Office for National Statistics. The data is augmented by splicing with growth rates from the ORBIS firm level database by Bureau van Dijk covering over one million firms, aggregated up to NUTS2 via a matching of city postcodes.

Regional Gross Fixed Capital Formation. Initial stock of capital for transport and housing is provided at the NUTS2 level and includes an industry breakdown. Source: Office for National Statistics.

Labor productivity at NUTS2 level is measured as the change in real output per worker between 2010–14 and 2003–07. Source: Office for National Statistics.

Smoking status at time of delivery. Data is from the website of Public Health of England, www.phoutcomes.info at the NUTS2 level.

Prevalence of obesity. Data source is the Public Health of England, www.phoutcomes.info at the NUTS2 level.

Tables

Table A.1.

Real public spending pre- and post-crisis 1

(Per head for health and economy and per pupil for education)

article image
Author’s estimates based on official and indexed UK data.

Aggregated up from NUTS-2 or 3 to NUTS-1 level.

Table A.2.

Outputs pre- and post-crisis 1

(Per head for health and economy and per pupil for education)

article image
Author’s estimates based on official UK data.

Aggregated up from NUTS-2 or 3 to NUTS-1 level.

Table A.3.

Robustness: Alternative Public Sector Efficiency Indicators 1

article image

A constant returns to scale technology is assumed in the estimation of the technical efficiency of the production function.