Estimation of Drivers of Public Education Expenditure: Baumol

WP/15/178
Estimation of Drivers of Public Education Expenditure: Baumol’s Effect Revisited
by Manabu Nose
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

© 2015 International Monetary Fund

WP/15/178

IMF Working Paper
Fiscal Affairs Department
Estimation of Drivers of Public Education Expenditure: Baumol’s Effect Revisited
Prepared by Manabu Nose1
Authorized for distribution by David Coady
July 2015
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
Abstract
This paper analyzes drivers of rising per-pupil public education spending, including Baumol’s “cost disease” effect. Higher wages paid to teachers contributed significantly to the increase in per-pupil spending over the past decades. Empirical analyses using a large dataset of advanced and developing economies show that the contribution of Baumol’s effect was much smaller than impled by theory. Rather, the spending inccrease reflects rising wage premiums paid for teachers in excess of market wages, especially in middleincome countries. The strong wage premium effect suggests that institutional characteristics that govern teachers’ wage setting are key determinants of education expenditure.
JEL Classification Numbers: H52, I21, I25, I28
Keywords: Public education expenditure, Baumol’s effect, wage premium, institutions
Author’s E-Mail Address: [email protected]
1 The author is grateful to Vitor Gaspar, Sanjeev Gupta, David Coady, Masahiro Nozaki, Baoping Shang, Benedict Clements, Julio Escolano, Lonkeng Constant, Junji Ueda, Joana Pereira, and seminar participants at the IMF’s Fiscal Affairs Department for insightful comments. The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management.

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Contents

Page

Abstract ......................................................................................................................................1
I. Introduction ............................................................................................................................4
II. Background ...........................................................................................................................6 A. Decomposition of Changes in Public Education Spending ......................................6 B. Decomposition of Changes in Per-Pupil Education Spending..................................7
III. A Model ...............................................................................................................................9
IV. Data and Descriptive Analysis...........................................................................................12 A. Wage Premium in Education ..................................................................................13 B. Summary Statistics ..................................................................................................14
V. Testing the Baumol’s Hypothesis .......................................................................................16
VI. Robustness Checks ............................................................................................................18 A. Intertemporal Parameter Stability ...........................................................................18 B. Asymmetry by Country Groups ..............................................................................19 C. A Comparison between Basic and Higher Education Expenditure.........................21
VII. Conclusion........................................................................................................................22

Tables
1. Summary Statistics...............................................................................................................15 2. Estimates of First-Differenced Model .................................................................................17 3. Intertemporal Parameter Stability ........................................................................................18 4. Asymmetry by Country Groups...........................................................................................20 5. Asymmetry by Basic and Higher Education Spending........................................................22

Figures
1. Decomposition of Change in Education Spending, from 1997–99 to 2007–09 ....................6 2. Decomposing the Change in Per-Pupil Education Spending, from 1997–99 to 2004–06.....8 3. Evolution of Student-Teacher Ratio ......................................................................................9 4. Testing the Common-wage Growth Assumption ................................................................12 5. The Ratio of Teachers’ Salaries Relative to Manufacturing Wage .....................................13 6. Evolution of the Adjusted Baumol’s Variable.....................................................................15

Appendices 1. Data Description ..................................................................................................................24 2. Panel Unit Root and Cointegration Test ..............................................................................26 3. Wage Premium in Public Education: Case Studies in Indonesia and South Africa ............29

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Appendix Tables 1. Main Sample ........................................................................................................................25 2. Panel Unit Root Test Result.................................................................................................26 3. Johansen Panel Cointegration Test ......................................................................................27 4. Vector Error Correction Model Estimate.............................................................................28 5. Wage Gap at Different Quantiles in Indonesia and South Africa........................................31 6. Decomposition Estimates in Indonesia and South Africa....................................................31
Appendix Figures 1. Median Wage Gap between Public Education and Manufacturing Sector: by Level of Education Attainment ..............................................................................................................32
References ................................................................................................................................33

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I. INTRODUCTION
Public education expenditure (PEE) constitutes a sizable part of a country’s budget, and the unit cost of public education continues to rise at varying degrees around the world. At the primary and secondary school level, data from the United Nations Educational, Scientific and Cultural Organization (UNESCO) indicate that the cost has been rising persistently and, in some countries, dramatically, during the last decade. In advanced economies, the average per-pupil primary and secondary education expenditures were 18 percent and 23 percent of per capita GDP in 1995–99, which steadily rose to 20 percent and 25 percent in ten years (Wolff, Baumol, and Saini, 2014). The cost of public education has also risen in emerging and developing countries and will rise in low-income countries for the promotion of universal coverage of public education to achieve the policy target under the Millennium Development Goals (MDG).
Besides concerns about rising costs of education, growing evidence suggests that public education needs to be more efficiently delivered to promote long-term economic growth. Despite significant increases in budget allocation to education, educational performance has not improved. Some studies found critical inefficiency in public education especially in sub-Saharan African countries (Gupta and Verhoeven, 2001; Herrera and Pang, 2005; Grigoli, 2014) which suggests that public education spending could be made more cost effective.
To address these concerns, policymakers need to understand determinants of the past increase in unit cost of public education. Does the cost increase reflect demographic change or institutional factors, such as a change in education policy, wage setting, and recruitment of teachers? In public health, previous studies found that only one-fourth of the past increase in the cost of medical care can be explained by demographic factors. The rest of the cost growth (known as excess cost growth) appears to come from non-demographic factors including progress in medical technology, the Baumol’s effect, and the change in health policies and institutions (IMF, 2010).2 In education, however, the factors driving higher per-pupil public education spending are still a black box.
In Baumol (1967), the service sector, such as education, is categorized as non-progressive industries that are characterized as being labor intensive in contrast to progressive (e.g., manufacturing) industries. In non-progressive industries, wage rates increase in proportion to higher wage rates in the progressive sector to retain workers despite low productivity growth (similar to the Balassa-Samuelson effect), driving up the unit cost of services in the non-progressive sector. As demand for education tends to be price inelastic, this triggers a continuous rise in public expenditure on education (the Baumol’s “cost disease”
2 These works include Medeiros and Schwierz (2013), Hartwig (2008), Carrion-i-Silvestre (2005), and Gerdtham and Lothgren (2000).

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hypothesis).3 The recent finding by Wolff, Baumol, and Saini (2014) suggests the existence of Baumol’s disease in public education for Organization for Economic Cooperation and Development (OECD) countries, but the Baumol’s hypothesis has not been examined in public education for emerging and developing countries.
Against this background, this paper examines driving factors of higher unit cost of public education. First, following a standard decomposition formula used in IMF (2014), it further decomposes an increase in per-pupil public education expenditure into price effect (teachers’ salaries), demographic effect (teacher-pupil ratio), and other educational spending. Second, it provides a theoretical and empirical framework to identify drivers of per-pupil public expenditure including the Baumol’s effect, for a sample of advanced and emerging economies much larger than that used by Wolff, Baumol, and Saini (2014). Finally, it provides sensitivity analyses to accounts for the asymmetry of these estimates by the country’s income level, the quality of the public education system, and different levels of education spending (basic education vs. tertiary education).
The main results are summarized as follows. The decomposition analysis finds that the historical increase in per-pupil PEE has been driven by the growth of teachers’ salaries. The regression analysis shows that Baumol’s effect is much weaker than suggested by the theoretical model as well as what was found for medical care spending (see Hartwig, 2008). Instead, the rising wage premium paid to teachers in public schools (in excess of the manufacturing sector wage) contributes more significantly to the growth of per-pupil PEE. The wage premium effect on higher unit cost of public education is found to be stronger in the middle-income countries and for countries with larger classroom size. The wage premium effect drives higher cost growth in basic education, while the Baumol’s effect and growth of capital education expenditure contribute more to the cost growth in tertiary education. Finally, two country case studies (in Appendix III) suggest that the rising wage premium for teachers may reflect institutional characteristics that govern teachers’ wage setting.
This paper is organized as follows. Section II provides the decomposition analysis on the changes in public education spending in recent years. Section III presents the modified Baumol’s model, and Section IV provides descriptive analysis on the data. Sections V and VI carry out empirical analysis. Section VII concludes.
3 Nordhaus (2008) provides evidence that the Baumol’s cost disease hypothesis holds in the U.S. based on the industry account data from the Bureau of Economic Analysis for the period 1948-2001. See Baumol, Ferranti, and others (2012) for the analysis on the fast-rising prices of health care and education in the United States and other advanced economies.

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II. BACKGROUND
A. Decomposition of Changes in Public Education Spending
Many countries around the world have scaled up budget allocations to education since the late-1990s. Following a standard decomposition formula as defined below, a public education spending-to-GDP ratio can be decomposed into three components: (a) school-age population (as a percent of working-age population), (b) school enrollment (also called education coverage), and (c) per-pupil spending on education (as a percent of GDP per worker).

While the ageing demographic trend reduces demand for public education, Figure 1 demonstrates that public education spending continued to increase owing to an increase in per-pupil education spending in many emerging countries (emerging Asia, Central Eastern Europe (CEE), and the Commonwealth of Independent State (CIS), and Latin America). In the sub-Saharan African (SSA) region, the school enrollment rate has significantly improved since the late-1990s, thereby pushing up public education spending in percent of GDP. Although the driver of public education spending (in percent of GDP) differs across regions, this figure reveals that per-pupil public education expenditure (PEE) has been the key driver of an increase in public education expenditure as similarly found by IMF (2014).

Figure 1. Decomposition of Change in Public Education Spending, from 1997–99 to 2007–09
(Percent of GDP)

1.2

Change in education spending (Percent of GDP)

0.7

0.2

-0.3

-0.8

Advanced CEE-CIS Emerging

LAC

Asia

MENA

School age population

School enrollment

Per-pupil spending

Change in education spending

SSA

Sources: UNESCO, UN, World Development Indicators, and IMF staff calculations.
Note: All ratios are median values of countries in each region. CEE-CIS = Central and Eastern Europe and the Commonwealth of Independent States; LAC = Latin America and the Caribbean; MENA = Middle East and North Africa; and SSA = SubSaharan Africa.

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B. Decomposition of Changes in Per-Pupil Education Spending
To clarify the key factors driving higher per-pupil PEE, this section further decomposes per-pupil PEE (the third component of the above identity equation) into the increases in (a) teachers’ salaries, (b) teacher-pupil ratios, and (c) other spending items as follows:
=
=
where w is teachers’ salaries and is the teacher-pupil ratio. Figure 2 shows the decomposition of the change in PEE into the change in and other spending realized between 1997–99 and 2004–06.
The figure shows that the growth of per-pupil PEE has been driven by an increase in teachers’ salaries for all regions. Teachers’ salaries in real terms tended to grow faster than labor productivity in advanced and non-advanced economies. In advanced economies, teachers’ salaries generally grew from higher willingness to pay for public education. In developing countries, teachers had been underpaid compared with market wages, reducing teachers’ motivation and driving them to opt out for other sectors (UNESCO, 2011).4 For example, in emerging European countries (such as Czech Republic, Iceland, Latvia, and Slovak Republic), teachers’ salaries used to be set lower than market wages.5 A large negative wage gap also existed for teachers in public schools in Latin America (Mizala and Nopo, 2012) and sub-Saharan Africa. In recent years, however, teachers’ salaries have been raised, significantly narrowing the wage gap, for example in Latin American countries (such as Nicaragua, Peru, Uruguay, and Ecuador).
For emerging economies in Asia, Middle East and North Africa (MENA) and Latin America and the Caribbean (LAC), the growth of other current and capital expenditure also contributed to higher per-pupil PEE. This could reflect the scarcity of education materials and teaching facilities in public schools as commonly observed in many developing countries.
4 Advanced economies in this study comprise 27 countries: Australia, Austria, Belgium, Canada, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, South Korea, Luxembourg, Malta, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States. 5 In several European countries (e.g., Estonia, Greece, Hungary, Ireland, and Spain), teachers’ salaries were cut or frozen to deal with downturns during the 2008–09 global financial crisis.

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Finally, a higher teacher-pupil ratio also led to higher per-pupil PEE especially in advanced economies and the CEE-CIS regions (Figure 2). In both groups, the teacher-pupil ratio has risen with the decline in school-age population (owing to population ageing) while the number of teachers remained unchanged, leading to an overstaffed and relatively inefficient public education system.6 The contribution is smaller in developing countries where the school-age population continues to expand and the classroom tends to be oversized (more than 20 students per teacher), notably in the sub-Saharan African countries (see Figure 3).

Figure 2. Decomposing the Change in Per-Pupil Education Spending, from 1997–99 to 2004–06
(Percent of GDP per capita)

20

15

Change in per-pupil spending (Percent of GDP per capita)

10

5

0

-5

Advanced CEE-CIS Emerging

LAC

Asia

MENA

Change in teacher salary

Change in teacher-pupil ratio

Change in other spending

Change in per-pupil spending

SSA

Sources: UNESCO, World Development Indicators, ILO, and IMF staff calculations.
Note: For each country, the median value of each variable during the time period (1997–99 and 2004–06) is computed. The bar chart depicts the distribution of the change in each component (median) across regions.

6 For example, in Portugal, the recurrent cost of public education is large. About 95 percent is spent on compensation for teaching and non-teaching staffs (IMF, 2013).

9 Figure 3. Evolution of Student-Teacher Ratio

Source: World Development Indicators.

III. A MODEL
Based on stylized facts in Section II, this section lays out a modified Baumol’s growth model (Baumol, 1967) to establish a hypothesis on the key drivers of per-pupil public education expenditure (PEE) growth.

Let us first divide the economy into two sectors: non-progressive (sector 1) and progressive

(sector 2). In this paper, the non-progressive sector is the education sector. We assume that

the productivity of the non-progressive sector could grow at the rate of (on account of an

improvement in delivery of public education for better technology, materials, teachers, and

class environment). On the other hand, the productivity in the progressive sector grows

faster, at rate (

). For simplicity, the model assumes out capital in the production

function.

(1)
with and as quantities of labor employed in the non-progressive and the progressive sectors, respectively, and a and b as constants.
Following the classic Baumol’s unbalanced growth model, nominal wages in both sectors are related in the long run and grow at labor productivity growth in the progressive sector