metadata: authors: 0: author: Chen,Dandan
count: Indonesia
docna: 0: docna: The economics of teacher supply in Indonesia
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Educational Institutions & Facilities,Effective Schools and Teachers,Rural Labor Markets
teratopic: Education
abstracts: cdata!: This paper examines the phenomenon of
            the over-supply of teachers but shortage of qualified
            teachers in Indonesia. Using a theoretical framework of
            government-dominated market with government-set wage rate
            and demand for teachers, the analysis explores how teacher
            supply, particularly the composition of the teaching force
            with low or high qualification, would be determined by
            current and future public policies. Using 2001 to 2008
            Indonesian Labor Force Survey data, the paper further
            estimates the potential effect of the most recent teacher
            law, which could give college educated teachers a
            significant pay increase, on the composition of the
            Indonesian teaching force with differentiated education
            backgrounds. Using a sample of workers with college
            education, the author finds that the relative wage rate of
            teachers and that of alternative occupations significantly
            influence the decision of college educated workers to become
            teachers. It is also found that the wage rate set by the
            most recent teacher law would increase the share of teachers
            approximately from 16 to 30 percent of the college-educated
            labor force. This increase that is due to the new
            government-set wage rate, would result in a pupil-teacher
            ratio of 24 to 25 pupils per teacher with college education,
            but will require a more than 31 percent increase in the wage
            bill for teacher salaries. The empirical approach of this
            paper is derived from a structural model that takes into
            account the endogeneity of the wage rate and corrects for
            sample-selection bias due to occupational choice.
geo_reg_key: 517198,517191
repnme: repnme: The economics of teacher supply in Indonesia
display_title: The economics of teacher supply in Indonesia
listing_relative_url: /research/2009/06/10710896/economics-teacher-supply-indonesia
topicv3: Teachers Management m1326119 1523,Labor Markets m1326488 855,Education m1327048 470,Jobs m1503931 2670,Labor Supply m1504055 2751
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Education
no_of_pages: 31
author_keys: 0: author_key: 452883
model_extractions: WPS4975 Policy Research Working Paper 4975 The Economics of Teacher Supply in Indonesia _Dandan Chen_ The World Bank East Asia and Pacific Region Human Development Sector Department June 2009
Policy Research Working Paper 4975

# **Abstract**

This paper examines the phenomenon of the oversupply of teachers but shortage of qualified teachers in Indonesia. Using a theoretical framework of governmentdominated market with government-set wage rate and demand for teachers, the analysis explores how teacher supply, particularly the composition of the teaching force with low or high qualification, would be determined by current and future public policies. Using 2001 to 2008 Indonesian Labor Force Survey data, the paper further estimates the potential effect of the most recent teacher law, which could give college educated teachers a significant pay increase, on the composition of the Indonesian teaching force with differentiated education backgrounds. Using a sample of workers _with college education,_ the author finds that the relative wage rate of teachers and that of alternative occupations significantly influence the decision of college educated workers to become teachers. It is also found that the wage rate set by the most recent teacher law would increase the share of teachers approximately from 16 to 30 percent of the college-educated labor force. This increase that is due to the new government-set wage rate, would result in a pupil-teacher ratio of 24 to 25 pupils per teacher with college education, but will require a more than 31 percent increase in the wage bill for teacher salaries. The empirical approach of this paper is derived from a structural model that takes into account the endogeneity of the wage rate and corrects for sample-selection bias due to occupational choice.

This paper—a product of the Human Development Sector Department, East Asia and Pacific Region—is part of a larger effort in the department to strengthen evidence-based human development policy advisory and dialogue. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dchen1@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Economics of Teacher Supply in Indonesia**<sup>**1**</sup>

Dandan Chen

> 1 The author thanks Imam Setiawan for the excellent research assistance.
# **I. Introduction and literature review**

Teachers are not only a major determinant of student learning, but also absorb a large amount of public resources. The supply of teachers has attracted much attention in academic research as well as in public policy debate. Dolton (2006) gave a comprehensive review of the existing empirical work on teacher supply issues, including entering teaching as the first job, staying in teaching, changing jobs and exiting teaching, and re-entering teaching. A majority of the existing literature on teacher supply is on developed countries, such as the US and the UK, where teacher supply issues have received a high level of attention, matched with rich micro and time-series data. One major research area is the determinants of the choice for teaching as an occupation, and particularly the effects of the differentiated earnings of teaching and non-teacher occupations.

Manski (1985)’s early work examined the relationship between academic ability, earnings, and the decision to become a teacher through analysis of data from a national sample of college graduates in the US. He found that the frequency of choice of teaching as an occupation is inversely related to academic ability. In the meantime, the earnings of teachers tend to rise only slightly, if at all, with academic ability. An econometric analysis suggests that in the absence of a minimum ability standard, increases in teacher earnings would yield substantial growth in the size of the teaching force but minimal improvement in the average academic ability of teachers. The average ability of the teaching force can be improved and the size of the teaching force maintained if minimum ability standards are combined with sufficient salary increases.

2
Flyer and Rosen (1997) examined the effects of the rising labor force participation of women on both demand for and supply of teachers. They concluded that in the US, the rising demand for teachers during the past few decades is attributable to the growing market opportunities for women and thus higher demand for a market substitute for home schooling. Widened occupational choice for women has also attracted women away from the traditionally female dominated occupations. Nonetheless, the flexibility of teaching and less wage penalty for temporary leaves remain important attractions for women to work in teaching jobs during their productive years.

Using the US longitudinal survey of the High School Class of 1972, Van der Klaauw (1997) estimated a dynamic utility model of occupational choice and occupational mobility, accounting for first and subsequent occupation choice. He found that teacher salaries and opportunity wages are important determinants of the supply and retention of teachers. Csellack (2002) also used a dynamic structural model and panel data (NLSY 1979, US) looking into teacher occupational choice. The simulation results show that a 2 percent increase in teachers’ wages increases teacher supply by 2.6 percent.

Hanushek and Pace (1995), on the other hand, find that in the US, participation in teacher training is not significantly affected by relative teacher earnings. In addition, teacher candidates perform lower on tests than other graduates, and teacher training completion is lowered by state requirements for courses and teacher tests.

Using UK data, Dolton (1990) and Dolton and Chung (2004) focused on looking into the problem of recruiting graduates into the teaching profession and retaining them in the UK. Dolton (1990) found that relative earnings in teaching and non-teaching occupations and the corresponding growth in earnings in these two choices have a 3
marked effect on graduates’ choices. In particular, the lower are the relative wages or wage growth in teaching, the less likely is a graduate to choose that career. These earning effects operate on initial choices and choices made later in an individual’s career. Dolton and Chung (2004) compared the earnings of qualified teachers who choose to teach with the opportunity wage for those who do not teach. They find that the rate of return on career choice for teachers has been declining for both men and women over the past 25 years although teaching is still relatively well paid for women.

There are also a few studies on the earnings effect on the aggregated teacher labor market. Thomas (1975) uses UK time series data between 1962 and 1970, and finds significant salary effects suggesting a 1 percent fall in relative starting salaries will induce a 2-5 percent fall in the relative supply of male graduates entering teaching and similar effects for average salaries of teachers. Effects for female graduates are up to twice as big. Zabalza, Turnbull and Williams (1979) also used UK time series, and estimated that the elasticity of graduate new entrants with respect to wage equals 2.4-3.9 for men and 0.3-1.8 for women; and that with respect to relative starting wages, it is 3.4 for men and 2.8 for women. Court et al. (1995) updated the time-series study in the UK, extending the study period to 1986-1992. The salary effect is still strong, suggesting that a 1 percent fall in relative starting salaries will induce a 4 percent fall in the relative supply of graduates entering teaching.

One recent study outside the US and UK is by Ortega (2006). Using Venezuelan data, his results suggest that the teacher wage premium and wage dispersion have little effect on the quality mix of applicants to teaching. Most students’ preference for teaching 4
is unresponsive to wage levels relative to other occupations and to wage growth prospects within teaching.

# **II. Indonesian context**

The overall teacher supply seems sufficient in Indonesia, with the pupil-teacher ratio declining steadily in recent years (Figure 1). The current pupil-teacher ratio is estimated at 19:1 for the primary level and 13:1 for the secondary level. These ratios clearly indicate that at an aggregated level, Indonesia does not have teacher shortage issues, unlike some other countries in the region, such as the Philippines or Cambodia. As a matter of fact, its pupil-teacher ratios are close to those of some developed countries such as Japan (20:1 at primary level, 13:1 at secondary level), or the US (15:1 at primary level and secondary level)<sup>2</sup> . Based on a school survey, the World Bank (2007) reports that a large proportion of Indonesian schools has an over-supply of teachers even according to the existing generous staffing norm. The report further points out that there is some space to improve the efficiency of the staffing norm. Based on the recommended formula, nearly 85 percent of the schools are over-staffed.

> 2 Edstats online database.

5
**Figure 1: Pupil-Teacher Ratio in Indonesia: 2001-2006**

<!-- Start of picture text --> 24.0<br>22.2<br>22.0<br>21.0 20.7 20.7<br>20.0 19.5<br>19.0<br>18.0<br>16.0 15.7 16.0<br>15.3<br>14.0 13.9 13.1 13.5<br>13.5 13.7 13.7 13.4 12.5<br>12.0<br>10.0<br>2001 2002 2003 2004 2005 2006<br>primary junior secondary senior secondary<br><!-- End of picture text --> However, in recent years, concern with the composition of the teaching force has been more prominent, particularly with the general recognition that learning outcomes are influenced by the ability of teachers who guide the learning process. There is an often expressed dissatisfaction with the distribution of ability within the present teaching force, with the public sentiment that public policies should induce desirable changes. With this line of thought, teachers’ minimum qualification, in terms of educational attainment, has been raised several times during the past decade, even though there is little consensus on whether educational attainment is a good measure of “ability” relevant for teachers.

The most significant policy change in teacher employment is the latest Teacher Law (UU14/2005) passed in 2005. It caught much attention with the 100 percent teacher salary increase for certified teachers. One of the key requirements for certification is four or more years of college education (S1 or D4). The Ministry of Education set the goal that by 2015, the whole teaching force should be comprised of only certified teachers. The magnitude of the potential change will be large given that a large proportion of incumbent teachers are below the minimum qualification level (Table 1). For example, by early 2006, over 80 percent of the primary school teachers were without college education. Given that there are nearly 2.7 million teachers in 6
Indonesia, and about half of them are primary school teachers, this translates to a large number of teachers that need to be upgraded or replaced.

# **Table 1: Number and percentage of teachers below required qualification before** **<u>and after the new teacher law</u>**

||Primary|Junior<br>secondary|Senior<br>secondary|
|---|---|---|---|
|**Minimum qualification starting in**<br>**2005**|**4-year degree**<br>**or diploma**|**4-year degree or**<br>**diploma**|**4-year degree**|
|% of teachers below min. qualification|<br>82%|<br>33%|13%|
|**Minimum qualification before 2005**<br>% of total teachers below min.<br>qualification|**2-year diploma**<br>33%|**3-year diploma**<br>15%|**4-year degree**<br>13%|

While the Ministry of Education is carrying out a massive upgrading program with recognition of prior experience for incumbent teachers, whether the law will be successful in the longer run depends foremost on how it would influence the occupational choice decision of college-educated workers. Until now, there has been no basis for making such forecasts. In the absence of empirical analysis, we can only guess at the impact of changes in teacher salaries on the composition of the teaching force.

Under a theoretical framework with a government-dominated market with a government-set wage rate and demand for teachers, this paper explores how teacher supply, particularly the resulting composition of the teaching force with low or high qualification, would be determined by current and future public policies. Using 2001 to 2008 Indonesian Labor Force Survey (SAKERNAS) data, this paper further estimates the potential effect of the latest teacher law, which gives high qualification teachers a significant pay increase, on the composition of the Indonesian teaching force with differentiated education backgrounds.

7
# **III. Theoretical framework: Aggregate teacher labor market**

Following Zabalza, Turnbull, and Williams (1979), the labor market for teachers can be thought of within a traditional supply and demand framework, with the additional complication that the government is virtually the sole hirer of labor. The demand for teachers can be determined by the number of children in the country of school age, and the government’s desired pupil–teacher ratio. For a given such ratio, the demand for teachers is therefore a constant, denoted by _q_ ∗ in Figure 2. Under the reasonable assumption that the supply of teachers with college education is a positive function of average teacher earnings, an upward-sloping labor supply schedule can be drawn as _S_ . In a perfectly competitive market, a teacher wage of _w_ ∗ would therefore clear this labor market.

**Figure 2: The labor market for teachers**

<!-- Start of picture text --> S<br>w *<br>W<br>D<br>Q1 q * Q2<br><!-- End of picture text --> 8
However, the teachers’ labor market is of course not competitive, and the government, in its role as (almost) exclusive purchaser of teaching labor, has other considerations, prime among which is the level of expenditure on teachers’ salaries in total. For a given level of such expenditure, an inverse relationship can be plotted between teachers’ earnings and the number of teachers hired, labeled _D_ in Figure 2. If the government wants to raise the salaries of teachers, it can afford to hire fewer of them, given a fixed budget.

Another complication added to the model is that teacher salaries do not adjust freely, as a majority of the teachers are civil servants, and therefore in most cases, their salaries follow a country’s civil servant remuneration scale.

Figure 2 illustrates a general case of how an aggregated teacher labor market would work. With a mostly fixed wage rate _W_ , the government can afford to hire Q2 teachers at this salary level. However, the market supply of teachers with college education at this salary level could be only Q1. Therefore the government can hire (Q2Q1) low qualification teachers. Q2 can be higher than what is the actual need for teachers _q_ *, as in this case, and therefore over-supply of total number of teachers, measured as (Q2- _q_ *), can co-exist with the shortage of teachers with college education, measured as ( _q_ *-Q1). This seems to explain the situation in Indonesia. Among all teachers Q2, the proportion of qualified teachers is thus (Q1/Q2), while that of under-qualified teachers is (Q2-Q1)/Q2.

On the other hand, if the government wants to hire all needed teachers ( _q_ *) with college education, the wage rate then needs to be set at _w_ *. However ( _q*, w*_ ) could be above the fiscal capacity. This case is illustrated in Figure 3: the government would have 9
the choice of either hiring only _q_ 0 college-educated teachers, or lower the wage rate to _W_ ’ to meet the demand for teacher numbers but with lowered average qualifications, with Q1’ teachers with college education, and (q*-Q1’) teachers under qualified.

**Figure 3: Labor market for teachers: low public budget**

<!-- Start of picture text --> S<br>w*<br>w ’<br>W<br>D<br>Q1 ’ q * Q2<br>q0 Q 1<br><!-- End of picture text --> The other scenario is that ( _q*, w*_ ) is below the fiscal constraint line _D_ , as illustrated in Figure 4. In this case, increasing the teacher salary level to _w_ * would reduce the total excess of teachers, retain the right number of teachers with college education, as well as save public resources. However, in reality, if this is the case, there would usually be an upward pressure to further raise teacher salaries beyond the necessary level, or hiring more teachers, until the total budget allocation is spent. These can be illustrated by the move upward of teacher salary from _w_ * to _w_ ’’, or hiring _q*_ ’ teachers rather than _q*_ .

10
**Figure 4: Labor market for teachers: high public budget**

<!-- Start of picture text --> S<br>w ’’<br>w*<br>W<br>D<br>Q1 q * q *’ Q2<br><!-- End of picture text --> For public policies that aim at inducing changes in aggregated teacher profile by changing teacher remuneration, key information is needed on the slope of _S_ , and the level of _w_ * that can induce the desired level _q_ *.

# **IV. Estimating teacher supply**

The econometric model that this paper uses is based on Dolton (1990), which modifies that of Zabalza et al. (1979) and Willis and Rosen (1979), and considers that there are two possible outcomes to a college graduate’s decision. The graduate can either decide to become a teacher ( _a_ ) or not ( _na_ ). It is assumed that the earnings streams in these two regimes may be parameterized by a simple geometric process. In this model, decisions are considered from the perspective of life-cycle earnings.

11
Consider an individual chooses to enter teaching at time _T_ then the present value of his expected earnings stream is: The maximum present value of earnings, chosen over alternative occupations is: Where: _W_ ( _t_ ) is earnings at time _t;_ ρ is “propensity to teach”; _s_ is the choice set of other occupations.

Similarly, define earnings profile for non-teachers as: where: _WT_ is the earnings at period _T_ , and _g_ is earnings growth rate.

Individual chooses not to go to teaching if _V_<sup>_na_</sup> _>V_<sup>_a_</sup> . Defining _I=ln(V_<sup>_na_</sup> _/V_<sup>_a_</sup> _),_ and plugging equation (3) and (4) into (1) and (2) would give: A linear approximation of equation (5) can be written as: 12
which is able to be estimated empirically.

This teacher supply framework has various drawbacks when applied to empirical work. One obvious limitation is that earnings data are rarely available across any appreciable time span in the life cycle. This poses problems for meaningful econometric analysis. This means that little can be said about life cycle earnings without some assumption concerning earnings growth. In addition, even though panel data can provide a few observations per individual at different points of time, the existing panel data sets in Indonesia (i.e. three waves of “Indonesia Family Life Survey”) do not have large enough samples for teachers, particularly of teachers with college education. In this paper, we use labor force survey data for a few consecutive years and control for yearspecific fixed-effects in earnings estimation for teachers and non-teachers.

The second limitation of the model is that the non-pecuniary rewards of teaching, or the individual’s “propensity to teach,” cannot be directly measured. Females with children may have higher propensity to become a teacher given that teaching is usually a more flexible job, and has less wage penalty when temporary leave happens (Flyer and Sherwin 1997). Dolton (1990) used the probability of having the first job as a teacher as a proxy for propensity to teaching when estimating occupation change between teacher and non-teacher at later stages of one’s career. However, this requires panel or historical data that is not easily available for the intended analysis. Even under this approach, equal non-pecuniary rewards to jobs were assumed for prior entry into any job any individual has. In this paper we include variables such as dummy for female, marital status, 13
household size in an attempt to capture the variations in individual’s preference for teaching.

The third key empirical difficulty is obviously the problem of how to estimate the foregone earnings in other occupations that influence an individual’s occupational decision, as one can only observe the earnings in the occupation that has been chosen. To solve this issue, we follow Dolton (1990)’s empirical approach involving 3-stages of estimation.

We need to estimate two earnings functions for teachers and non-teachers: Obviously, people are not randomly selected into teachers and non-teachers. OLS estimates would be biased. Sample selection bias can be corrected by starting with estimating a probit model of being a teacher: where _I_ equals 1 if an individual is a teacher, and 0 otherwise. _Z_ includes all exogenous variables.

The second stage estimates the log earnings function for teachers and nonteachers (eq. 7 and 8), by inserting Mill’s ratio ( λ ) on the right-hand side. For teacher and non-teacher sub-samples, the consistent estimates can be obtained by OLS estimates of the following: where 14
These first and second stages can also be estimated using maximum likelihood based on conditional distributions of earnings for teachers and non-teachers. The likelihood function of teacher earnings is The likelihood function for non-teacher earnings is: With consistent estimates of β 1 and β 2 , we use predicted values of _W_<sup>_a_</sup> and _W_<sup>_na_</sup> for each individual to estimate the structural model of the following:

# **V. Data and results**

We use the 2001-2008 Indonesia Labor Force Survey (SAKERNAS) to look into the occupational choice of working cohorts with college education. Our sample includes 40,019 workers with college education from year 2001 to 2008. Overall, only around 3% 15
of the 20-year old and above population are able to attain this level of education. The proportion increases overtime, but at very slow pace between 2001 and 2008 (Table 2). Table 2 also shows that teaching job is a prominent choice for college graduates.

Between 2001 and 2008, around one-fifth to a quarter of the college graduates are teachers.

**Table 2: Composition of labor force with college education**

|Year|non-<br>teacher|teacher|Total|% with college<br>education<br>among population<br>age 20 and above|
|---|---|---|---|---|
|2001|2,414|333|2,747|3.0%|
|2002|3,729|687|4,416|2.7%|
|2003|4,750|846|5,596|3.1%|
|2004|4,979|982|5,961|3.3%|
|2005|3,411|911|4,322|2.8%|
|2006|4,363|972|5,335|3.2%|
|2007|4,683|954|5,637|3.3%|
|2008|5,160|845|6,005|3.6%|
|Total|33,489|6,530|40,019|3.1%|

Figure 5 depicts the trend of relative earnings for teacher and non-teacher college graduates by age group. Earnings of teachers have been below that of non-teachers in for the past few years. However, the real earnings gap is narrowing. Teacher’s real earnings growth has been faster than that of non-teachers in recent years. A closer look reveals that teacher’s real earnings has been mostly constant over the years, while it is nonteacher’s earnings that has actually been eroded by inflation over time.

16
**Figure 5: Log real earnings of teachers and non-teachers with college education in Indonesia, by age group, 2002-2008**

<!-- Start of picture text --> 2002 2004<br>2006 2008<br>20-29 30-39 40-49 50-5920-29 30-39 40-49 50-59<br>age group<br>log real earnings of teachers log real earnings of non-teachers<br>Graphs by year<br>14.5<br>14<br>13.5<br>13<br>14.5<br>14<br>13.5<br>13<br><!-- End of picture text --> Figure 6 shows the trend of relative earnings (ratio) of teachers, and the share of labor force with college education that are teachers. Between 2001 and 2005, the share of college graduates shows an growing trend, reaching over a quarter of total workers with college education. Between 2005 and 2008, this trend seems to experience a reverse. By 2008, the share of college graduates on the labor market and who are teaching came back down to 19 percent.

For college graduates, even though the premium of non-teaching job is eroding over time, there were periods that show a reversed trend, such as between 2005 and 2008.

17
The earnings ratio of non-teachers to teachers increased slightly from 1.1 to 1.3. More of interest is the negative correlation between the pay-off of non-teaching job and the share of college graduates in teaching (Figure 7). The correlation is -0.83, and highly significant ( _P_ =0.01).

**Figure 6: Trend of relative earnings and share of college graduates in**

**teaching, 2001-2008**

<!-- Start of picture text -->
1.7 0.28<br>0.26<br>1.5<br>0.24<br>1.3 0.22<br>0.2<br>1.1<br>0.18<br>0.9 0.16<br>0.14<br>0.7<br>0.12<br>0.5 0.1<br>2001 2002 2003 2004 2005 2006 2007 2008<br>Wnt/Wt % of college graduates in teaching<br>Wnt/Wt teaching<br>% college graduates in<br><!-- End of picture text -->

_Source: SAKERNAS_ .

**Figure 7: Relative earnings vs. share of college graduates in teaching, 2001-2008**

<!-- Start of picture text --> 0.28<br>0.26<br>0.24<br>0.22<br>0.2<br>0.18<br>0.16<br>0.14<br>0.12<br>0.1<br>1 1.1 1.2 1.3 1.4 1.5 1.6<br>Wnt/Wt<br>% of college grad in teaching<br><!-- End of picture text --> 18
Now we turn to individual level data and estimate the effect of earnings differentials on the occupation choice of teachers versus non teachers, using the empirical framework laid out in the previous section. Table 3 is the descriptive sample statistics.

**Table 3: Sample summary statistics:**

|Variable<br>name|Definition|Mean|Standard<br>error|
|---|---|---|---|
|Dependant<br>variables:||||
|_Dteacher_|0-1 dummy variable, =1 if individual is a teacher|0.163|0.370|
|_Earnings_|Monthly earnings in Rupiah (Rp)|1,262,190|2,038,909|
|_lnW_|Log earnings, =ln(_Earnings_)|14.106|0.757|
|Explanator<br>y variables:||||
|_Age_|age|37.173|10.183|
|_Dfemale_|0-1 dummy variable, =1 if individual is female|0.396|0.489|
|_Drural_|0-1 dummy variable, =1 if individual lives in rural area|0.153|0.360|
|_Dmarried_|<br>0-1 dummy variable, =1 if individual is married|0.725|0.447|
|_D2001_|0-1 dummy variable, =1 if individual is from 2001 sample|0.069|0.253|
|_D2002_|<br>0-1 dummy variable, =1 if individual is from 2002 sample|0.110|0.313|
|_D2003_|0-1 dummy variable, =1 if individual is from 2003 sample|0.140|0.347|
|_D2004_|0-1 dummy variable, =1 if individual is from 2004 sample|0.149|0.356|
|_D2005_|0-1 dummy variable, =1 if individual is from 2005 sample|0.108|0.310|
|_D2006_|<br>0-1 dummy variable, =1 if individual is from 2006 sample|0.133|0.340|
|_D2007_|0-1 dummy variable, =1 if individual is from 2007 sample|0.141|0.348|
|_D2008_|<br>0-1 dummy variable, =1 if individual is from 2008 sample|0.150|0.357|
||<br>0-1 dummy variable, =1 if individual lives in Nanggroe Aceh|||
|_D11_|<br>Darusalam|0.026|0.158|
|_D12_|0-1 dummy variable, =1 if individual lives in Sumatera Utara|0.037|0.190|
|_D13_|<br>0-1 dummy variable, =1 if individual lives in Sumatera barat|0.028|0.165|
|_D14_|0-1 dummy variable, =1 if individual lives in Riau|0.017|0.128|
|_D15_|<br>0-1 dummy variable, =1 if individual lives in Jambi|0.011|0.103|
|_D16_|0-1 dummy variable, =1 if individual lives in Sumatera selatan|0.017|0.131|
|_D17_|<br>0-1 dummy variable, =1 if individual lives in Bengkulu|0.014|0.116|
|_D18_|0-1 dummy variable, =1 if individual lives in Lampung|0.014|0.116|
|_D19_|<br>0-1 dummy variable, =1 if individual lives in Bangka belitung|0.005|0.070|
|_D21_|<br>0-1 dummy variable, =1 if individual lives in Kepulauan Riau|0.006|0.079|
|_D31_|<br>0-1 dummy variable, =1 if individual lives in DKI Jakarta|0.228|0.419|
|_D32_|<br>0-1 dummy variable, =1 if individual lives in Jawa Barat|0.073|0.261|
|_D33_|<br>0-1 dummy variable, =1 if individual lives in Jawa Tengah|0.066|0.248|
|_D34_|0-1 dummy variable, =1 if individual lives in DI Yogyakarta|0.060|0.237|
|_D35_|<br>0-1 dummy variable, =1 if individual lives in Jawa Timur|0.113|0.317|

19
|Variable<br>name|Definition|Mean|Standard<br>error|
|---|---|---|---|
|_D36_|0-1 dummy variable, =1 if individual lives in Banten|0.024|0.152|
|_D51_|0-1 dummy variable, =1 if individual lives in Bali<br>0-1 dummy variable, =1 if individual lives in Nusa Tenggara|0.039|0.193|
|_D52_|Barat|0.020|0.140|
||0-1 dummy variable, =1 if individual lives in Nusa Tenggara|||
|_D53_|Timur|0.014|0.119|
||0-1 dummy variable, =1 if individual lives in Kalimantan|||
|_D61_|Barat|0.015|0.120|
||0-1 dummy variable, =1 if individual lives in kalimantan|||
|_D62_|Tengah|0.011|0.105|
||0-1 dummy variable, =1 if individual lives in kalimantan|||
|_D63_|Selatan|0.019|0.138|
||0-1 dummy variable, =1 if individual lives in Kalimantan|||
|_D64_|Timur|0.017|0.130|
|_D71_|0-1 dummy variable, =1 if individual lives in Sulawesi Utara|0.015|0.121|
|_D72_|0-1 dummy variable, =1 if individual lives in Sulawesi tengah|0.015|0.120|
|_D73_|0-1 dummy variable, =1 if individual lives in Sulawesi Selatan<br>0-1 dummy variable, =1 if individual lives in Sulawesi|0.035|0.184|
|_D74_|tenggara|0.017|0.127|
|_D75_|0-1 dummy variable, =1 if individual lives in Gorontalo|0.007|0.082|
|_D76_|0-1 dummy variable, =1 if individual lives in Sulawesi barat|0.003|0.058|
|_D81_|0-1 dummy variable, =1 if individual lives in Maluku|0.013|0.112|
|_D82_|0-1 dummy variable, =1 if individual lives in Maluku Utara|0.007|0.081|
|_D91_|0-1 dummy variable, =1 if individual lives in Papua barat|0.004|0.065|
|_D94_|0-1 dummy variable,=1 if individual lives in Papua'|0.011|0.106|

The sample includes the labor force with college education between 2001 and 2008. There are 40,019 observations in our sample, among which about 40 percent are female, and 16.3 percent are primary or secondary school teachers.

The results of the maximum likelihood estimates for college-educated teacher and non-teacher log earnings functions, corrected for self-selection, are presented in Table 4. The result shows that for teachers, controlled for age, there are no significant earnings differentials between urban and rural areas, or between male and female teachers. As mentioned in the previous section, this may reflect the fact that in Indonesia, a majority of teachers are civil servants, and follow standard pay scales.

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# **Table 4: Maximum likelihood estimates of** **_Log_ Earnings function for teachers and non-teachers**

||Teachers l|og ear|nings|Non-teacher|s log e|arnings|
|---|---|---|---|---|---|---|
||_Ln_|_Wt_||_Ln_|_Wnt_||
|Independent<br>variable|Coefficient||Standard<br>error|Coefficient||Standard<br>error|
|Constant|9.516|***|(0.141)|12.114|***|(0.071)|
|Age|0.153|***|(0.006)|0.078|***|(0.003)|
|Age<sup>2</sup>|-0.001|***|(0.000)|-0.001|***|(0.000)|
|Dfemale|-0.010||(0.016)|-0.023|*|(0.012)|
|Drural|0.024||(0.021)|-0.119|***|(0.017)|
|D2002|0.021||(0.039)|0.066|**|(0.025)|
|D2003|0.225|***|(0.037)|0.265|***|(0.023)|
|D2004|0.162|***|(0.037)|0.331|***|(0.023)|
|D2005|0.229|***|(0.037)|0.291|***|(0.025)|
|D2006|0.300|***|(0.037)|0.416|***|(0.024)|
|D2007|0.368|***|(0.037)|0.511|***|(0.024)|
|D2008|0.405|***|(0.037)|0.538|***|(0.024)|
||Censored obs|=|33,489|Censored obs|=|6,530|
||Uncensored|||Uncensored|||
||obs|=|6,530|obs|=|20,934|
||Wald<br>2<br>χ(11)|=|3,228.82|Wald<br>2<br>χ(11)|=|3,937.5|
||Prob ><br>2<br>χ|=|0.000|Prob ><br>2<br>χ|=|0.000|

_*** p<0.01, ** p<0.05, * p<0.1._ For workers in non-teaching jobs, however, there are significant earnings differentials. Equally college-educated, a worker in rural areas earns 12 percent less.

Woman also earns less than man. Even though the earnings differential between man and woman is small (2 percent), it is statistically significant.

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In addition, the earnings growth rate, as measured by the coefficient estimates on _Age_ , appears to be higher for teachers than for non-teachers: 15 percent per year for teachers as compared with 7.8 percent for non-teachers. However, a teacher’s earnings peak at age 54, while a non-teacher’s at age 62. This is possibly due to the mandatory retirement age at 55 for civil servant teachers.

Table 5 presents the structural and reduced form estimates of occupational choice, equation (9) and (10) respectively. To avoid relying solely on the non-linearity of the functional forms for identification, excluded variables need to be identified for the earnings function, and the structural function of the occupational choice. The province dummy variables are used for estimating the occupational choice function, but excluded from the earnings function. The argument could be that localized demand for teachers, due to various enrollment rates influence by local household socioeconomic conditions, can affect the likelihood of entering teaching profession, but not teacher earnings. On the other hand, the dummy variables for various years are included in the earnings function estimation to capture overall labor market shifts in labor costs from year to year, but are excluded from the structural estimates for occupational choice assuming the year-to-year labor market change only affects individual’s choice through changing earnings differentials.

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# **Table 5: Probit of choosing teaching by college graduates**

||Redu|ced for|m|Struct|ural fo|rm|
|---|---|---|---|---|---|---|
|Independent<br>variable|Marginal<br>effect||Standard<br>error|Marginal<br>effect||Standard<br>error|
|Age|0.002|***|(0.000)||||
|lnWnt_lnWt||||-0.295|***|(0.013)|
|Dmarried*|0.054|***|(0.004)|0.023|***|(0.005)|
|Dfemale*|0.084|***|(0.004)|0.083|***|(0.004)|
|Drural*|0.175|***|(0.006)|0.117|***|(0.006)|
|D11*|0.023|*|(0.013)|0.024|*|(0.013)|
|D12*|0.001||(0.011)|0.000||(0.011)|
|D13*|-0.014||(0.011)|-0.015||(0.011)|
|D14*|-0.014||(0.014)|-0.013||(0.014)|
|D15*|0.046|**|(0.020)|0.047|**|(0.020)|
|D16*|0.008||(0.015)|0.008||(0.015)|
|D17*|0.043|**|(0.018)|0.044|***|(0.018)|
|D18*|0.030|*|(0.017)|0.027|*|(0.017)|
|D19*|0.047|*|(0.029)|0.055|**|(0.030)|
|D21*|0.012||(0.025)|0.011||(0.025)|
|D31*|-0.081|***|(0.006)|-0.075|***|(0.006)|
|D33*|0.094|***|(0.012)|0.094|***|(0.012)|
|D34*|0.000||(0.010)|0.004||(0.010)|
|D35*|0.052|***|(0.009)|0.051|***|(0.009)|
|D36*|0.009||(0.014)|0.004||(0.013)|
|D51*|-0.012||(0.010)|-0.013||(0.010)|
|D52*|0.114|***|(0.018)|0.113|***|(0.018)|
|D53*|0.035|**|(0.017)|0.027|*|(0.017)|
|D61*|0.022||(0.017)|0.024||(0.017)|
|D62*|-0.021||(0.017)|-0.020||(0.017)|
|D63*|-0.013||(0.013)|-0.008||(0.014)|
|D64*|-0.012||(0.014)|-0.012||(0.014)|
|D71*|-0.030|**|(0.013)|-0.033|**|(0.013)|
|D72*|0.020||(0.016)|0.022||(0.016)|
|D73*|-0.018||(0.010)|-0.016||(0.010)|
|D74*|-0.005||(0.014)|-0.002||(0.014)|
|D75*|0.008||<br>(0.022)|0.007||<br>(0.029)|
|D76*|0.016||<br>(0.030)|0.007||<br>(0.022)|
|D81*|-0.006||(0.016)|-0.008||(0.016)|
|D82*|0.013||(0.024)|0.011||(0.023)|
|D91*|-0.039||(0.024)|-0.050|*|(0.022)|
|D94*|-0.049|***|(0.014)|-0.050|***|(0.014)|

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||Redu|ced fo|rm|Struct|ural form|
|---|---|---|---|---|---|
|Independent<br>variable|Marginal<br>effect||Standard<br>error|Marginal<br>effect|Standard<br>error|
|D2002*|0.016|*|(0.010)|||
|D2003*|0.043|***|(0.010)|||
|D2004*|0.042|***|(0.010)|||
|D2005*|0.054|***|(0.011)|||
|D2006*|0.023|**|(0.009)|||
|D2007*|0.008||(0.009)|||
|D2008*|-0.019|**|(0.008)|||
||Number of obs<br>|=|40,019|Number of obs<br>|=<br>40,019|
||LR:<br>2<br>χ (43)|=|3,103.83|LR:<br>2<br>χ(36)|=<br>3,329.76|
||Prob ><br>2<br>χ|=|0|Prob ><br>2<br>χ|=<br>0|

_*** p<0.01, ** p<0.05, * p<0.10._ The primary focus for the occupational choice model is the coefficient on the earnings differential variable ( _lnWnt - lnWt_ ), holding constant the other explanatory variables relating to various background and personal characteristics. A negative and significant coefficient on earnings differential variable would indicate that a collegeeducated worker is less likely to choose teaching as profession if other occupations pay better. Our estimate result shows the “right” sign and a high significance level. Several other clear effects are notable in predicting whether in teaching profession or not. Being a woman or being married is significantly correlated with being in teaching force. It also appears that the predominant job for a college-educated worker in rural area is teaching. The likelihood of being a teacher is 11 percentage points higher for a college graduate in a rural area than that in an urban area.

Based on the empirical results, double the salary of teachers with college education would result in an increase in the probability of college graduates choice of entering teaching force. The marginal effect would be: 24
This would increase the proportion of college graduates choosing teaching profession to about one-third. Using the population projection by BPS (Bureau of statistics), and assuming constant proportion of population above age twenty with college education, this would lead to about 24-25 pupils per teacher with college education. This level of pupil-teacher ratio is higher than the current level, but still falls within the adequate range compared with other countries in the region.

Assuming this can be an acceptable pupil-teacher ratio such as _q*_ in Figure 3, it still requires increased amount of public resources. With increased pupil-teacher ratio from 16:1 to 24:1, but doubled teacher remuneration, the per-pupil cost would increase 33 percent. Without this commitment, there will be either teacher shortage, or a mix of teachers with high and low education background will continue to exist.

# **VI. Conclusions**

Aiming at attracting high caliber human resources into teaching, the latest Teacher Law (UU14/2005) in Indonesia promises a 100 percent teacher salary increase for certified teachers with a minimum 4-year college education or above. In the long run, whether the law will be successful in attracting the needed college-educated labor force into teaching depends foremost on how it would influence their occupational choice decision. Until now, there has been no basis for making such forecasts. The findings of this paper provide some empirical foundations for the latest teacher law in Indonesia.

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This paper has analyzed Indonesian teachers’ labor supply under a theoretical framework that is based on a government-dominated market with government-set wage rate and demand for teachers. This framework could explain the phenomenon of the overall over-supply of teachers but shortage of qualified teachers in Indonesia. The results from the structural estimates constructed under the framework are particularly useful for looking into the impact of the latest teacher law on the future education profile of Indonesian teaching force.

Using a sample of workers with college education in the Indonesian Labor Force Survey, this paper has found that the relative wage rate of teachers and that of the alternative occupations significantly influence college educated workers’ decision of becoming teachers in Indonesia. The large-scale pay increase promised by the law for teachers with college education will have a significant effect on attracting a collegeeducated labor force to join the teaching force. It is estimated that the wage rate set in the latest teacher law will be able to increase the share of teachers approximately from 16 percent to 30 percent of the college-educated labor force. In addition, the new government-set wage rate can sustain a pupil-teacher ratio of 24-25 pupils per teacher with college education, but will require a more than 30 percent increase in the teacher salary bill.

Finally, we conclude by highlighting that attracting a high caliber labor force into teaching is from the quality concern in the first place. Even though there is general recognition that learning outcomes and education quality are influenced by the ability of teachers who guide the learning process, whether qualification in terms of educational attainment is an appropriate measure for teaching ability is debatable. Even though there 26
is established literature on the positive sorting of ability and educational attainment (Willis and Rosen 1979), the ability to be a good teacher may also be different from general academic ability. Furthermore, getting the right people to teach is only the vey first step in improving educational quality and learning outcomes. How to make these right people perform well and achieve results is an even a bigger challenge.

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