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Developing Asia and the World
Economic trends and prospects in developing Asia
Growth amid change

Conclusions

This chapter has examined the evolving distribution of education, wages, and employment in four Asian countries. It found education levels running ahead of employment growth in sectors that have traditionally hired the educated. Education levels rose in almost all sectors and occupations. This result is normatively ambiguous. Education levels may have risen within activities because they were too low from a productivity perspective to begin with. In this view, rising education levels should have increased productivity. Alternatively, education levels may have risen simply because education has become cheaper, unemployment is high, incomes are rising-or any number of other reasons unrelated to the productivity benefits of education.

It was therefore important to distinguish between two sets of explanations of education intensification: that it has been driven by productivity imperatives; or by something other than productivity imperatives. Under the assumption that worker productivity is reflected in wages, wage returns to education in specific sectors and occupations were therefore examined.

The returns to basic education have fallen, and now lie below the cost of funds in several sectors and occupations. Moreover, the sector this occurs in varies by economy. There is evidence that Filipino and Indian agricultural workers are overeducated, as are Filipino and (perhaps) Thai industrial workers. Furthermore, there are particular groups of workers, including Indian agricultural laborers, and drivers and maids everywhere, who receive a negligible return on their schooling. In the Philippines, where job creation has been anemic, the education levels and employment shares of drivers and maids are increasing. This is a sure sign that marginal educated workers reap no rewards for their schooling.

3.2.6 Education profile and wage indexes of retail sales staff

N = none; IP = incomplete primary; M = middle; P = primary; ILS = incomplete lower secondary; LS = lower secondary; HS = higher secondary; IT = incomplete tertiary; T = tertiary.

Note: Limited to those reporting wages.

Sources: India National Sample Survey Organisation, Socio-economic Survey, Schedule 10, 1993/94, 2004; Philippine Labor Force Survey, 1991, 2004, October rounds; Thailand Labor Force Survey, 1995, 2005, October rounds.
Click here for figure data
3.2.7 Education profile and wage indexes of clerical workers

N = none; IP = incomplete primary; M = middle; P = primary; ILS = incomplete lower secondary; LS = lower secondary; HS = higher secondary; IT = incomplete tertiary; T = tertiary.

Note: Limited to those reporting wages.

Sources: India National Sample Survey Organisation, Socio-economic Survey, Schedule 10, 1993/94, 2004; Philippine Labor Force Survey, 1991, 2004, October rounds; Thailand Labor Force Survey, 1995, 2005, October rounds.
Click here for figure data

However, the point of this discussion is not that education subsidies need to be scaled back. Overeducation can be resolved relatively quickly through declines in the cost of funds, and more occasionally by dramatic technological shifts. Rather, the point is that severe distortions must be hindering job creation and economic dynamism, thereby muting the pecuniary benefits of schooling.

It is often argued that it is in fact the limited supply of educated workers itself that is the constraint on economic dynamism. Surely growth of Filipino call centers and of IT-enabled services in India is hindered by a shortage of suitably educated workers? After all, call center managers informally interviewed for this work believe the employment opportunities in the sector to be substantial, if education bottlenecks can be overcome. This is certainly possible, and job creation in the sector would indeed bring benefits. However, there are at least three reasons against forging education policy based on the conclusion that raising target schooling levels will unleash transformation through high-end services.

 

First, as shown in this chapter, the employment shares of these sectors are extremely small, so projecting their capacity to create a large number of jobs involves tremendous out-of-sample extrapolation. Magtibay-Ramos et al. (2007) argue that to meet the forecast of the business process outsourcing (BPO) industry (and subsequently the Government) of 1 million BPO jobs in the Philippines by 2010, over one quarter of all new jobs would have to be in the industry-a historically unusual figure. Furthermore, other constraints, lurking just out of sight, may become apparent as the sectors expand. This is indeed one of the key lessons of the young literature on binding constraints to growth.

Second, the economic cost of bottlenecks should be considered. Certainly in India's high-end services, salaries have sky-rocketed to industrial-economy levels, and job retention is becoming harder as workers leave one job in the sector for more remunerative options elsewhere in the sector. In the Philippines call center business, the impact of education bottlenecks on productivity growth is much less clear. Call center salaries for college graduates range from $250 to $400 a month-or around two or three times GDP per capita-an attractive number, but perhaps less so for graduates of the nations' top universities. As constraints on growth go, this is not particularly expensive. Moreover, employers report rising rates of absenteeism and that worker retention is becoming a problem. Anecdotally speaking, employees frequently leave the profession in favor of less remunerative jobs that do not require them to work the "graveyard shift. "Thus, the wages paid by the sector overstate the welfare improvement for workers.

Third, it is not obvious that the skills required in all of these professions are best obtained through general education. Several call center managers have expressed the view that their employees

3.2.8 Education profile and wage indexes of bookkeepers/cashiers

N = none; IP = incomplete primary; M = middle; P = primary; LS = lower secondary; HS = higher secondary; T = tertiary.

Note: Limited to those reporting wages.

Sources: India National Sample Survey Organisation, Socio-economic Survey, Schedule 10, 1993/94, 2004; Indonesia SAKERNAS 1994, 2004; Thailand Labor Force Survey, 1995, 2005, October rounds.
Click here for figure data
3.2.9 Education profile and wage indexes of engineers and engineering technicians

N = none; IP = incomplete primary; M = middle; P = primary; LS = lower secondary; HS = higher secondary; T = tertiary.

Note: Limited to those reporting wages.

Sources: India National Sample Survey Organisation, Socio-economic Survey, Schedule 10, 1993/94, 2004; Thailand Labor Force Survey, 1995, 2005, October rounds.
Click here for figure data

probably learned their English outside the classroom. Reports are starting to trickle in that firms in both countries are entertaining reasonable alternatives to college-educated workers. Some are establishing in-house training programs and report being satisfied with the results, while others are seeking high school graduates with the requisite skills. Despite the sector's growth potential, these trends cast doubt on the relevance of this potential for education policy.

This chapter has therefore argued that policy advisers should price constraints to growth via the return to education before forging conclusions on education policy. This being done, it takes no issue with the view that some select groups of educated workers are in short supply and that this is a constraint to growth.

One final issue concerns the quality of education. Results from the labor force surveys are only representative of the quality of education typically available in each country. Certainly, raising the quality of education might increase the returns to schooling and probably would be helpful for igniting growth. Lacking data on school quality, this chapter cannot speculate on these issues. It is certainly possible-even likely- that higher-quality education will precipitate higher rates of investment and job creation, particularly in industry and services. This point made, and given that returns to the types of basic education available have fallen fast, this chapter concludes that it is mainly by measuring and credibly delivering improvements in quality that basic education projects could contribute directly to structural change and growth.

These caveats notwithstanding, the employment numbers provided are quite sobering, and caution against mechanically raising general education targets in the hope of generating growth. Certainly other reasons exist for raising education levels. But expectations of the contribution of education to structural change must be rooted firmly in a thoughtful, empirical understanding of what workers are likely to do with their education. The evidence provided suggests that when economies or sectors stagnate, the availability of jobs and new technologies may do more for many than more time in the classroom.

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Datasets used and sources

Country Source Date
India Socio-Economic Survey, Schedule 10, National Sample Survey Organisation, India 1993/94 (Round 50), 2004 (Round 60)
Indonesia SAKERNAS, Badan Pusat Statistik, Indonesia 1994, 2004
Philippines Labor Force Survey, National Statistics Office, Philippines 1991, 2004 October rounds
Thailand Labor Force Survey, National Statistics Office, Thailand 1995, 2005 October rounds

The authors are grateful to Guntur Sugiyarto for providing statistics from the Indonesian SAKERNAS labor force surveys.

 
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