Medium to long-term labor supply-demand forecast

MediuM to Long-terM LABOR SUPPLY-DEMAND

ForeCASt

2013

12000

10000

8000

6000

4000

2000

0

Billio

n tu

grik

2012 2012 2012 2012 20122022 2022 2022 2022 2022

Agriculture Mining and Quarrying Manufacturing Service GDP

8071272

976

2104705

1360 3010

5678

5,498.5

10,414.1

huMAn reSourceS DeveloPMent Service of koreA

MediuM to Long-terM LABOR SUPPLY-DEMAND FORECAST

1

We have developed a medium to long-term

labor market forecasting (pilot) model for

Mongolia for the first time. The timing of this

model development coincides with the structural

changes in population and the rapid economic

growth expected in the country which require

changes in labor policies on the labor force

participation rate and labor productivity.

We have forecasted major changes in the labor

market until 2022 in terms of 19 industries and

10 major occupational groups using the model.

One of the major objectives of labor policies is

to promote inclusive growth by developing the

national labor force. It implies to improve the

higher and vocational education system, and

labor productivity in industries.

On the other hand, labor studies provide

school leavers and the current labor force with

information on the choices of occupation and

directions to enhance their skills.

We will be working to promote the forecast

results for policy making and information

purposes. In 2014, we have two objectives to

improve the forecast. First, the forecast will be

based on the sub-classifications of industries

and sub-groups of occupations. As a result,

there will be more detailed information for a

policy making purpose. Second, we will consider

various policy scenarios so that we will be able to

forecast the effects of proposed policy changes

on the labor market outcomes.

During the period in which we publicized

the results of the pilot model, the President

of Mongolia initiated the manifesto on the

principles of a smart government and the

government reported that it would keep a policy

not to increase the number of government

employees. When we introduce these policy

changes in the model, the forecast results would

be quite different as the additional employees

in the government sector forecasted by the pilot

model would have to be allocated across the

other industries.

It is important to maintain the capacity building

taking place in the modelling and forecasting

sector at the Institute of Labour Studies and

develop its cooperation with other advisory

organizations.

I would like to thank the officials at the

Ministry of Labour of Mongolia and Ministry of

Employment and Labor of the Republic of Korea

who supported our work.

Foreword


2

I would also like to congratulate to Human

Resources Development Services of Korea

and “Gerege Partners” LLC on their successful

collaborations with us.

I hope that you will find the forecast results

useful for the purposes of policy making and

information providing leading to the efficient

allocation of national human recourses.

CHIMEDDORJ MUNKHJARGAL

Director of Institute for Labour Studies


3

Table of ConTenTs

Chapter 1. Medium to Long-term Labor Supply-Demand ForecastIntroduction and Method

1. Significance of labor supply-demand forecasting ............................................................. 5

2. Forecasting procedure and method ................................................................................... 5

3. Statistical data used for forecasting ...................................................................................7

4. Work required to be undertaken further............................................................................7

Chapter 2. Major Results of the 2013-2022 Medium to Long-term Forecast

1. Labor force forecast .......................................................................................................... 9

2. Employment forecast by industries .................................................................................. 16

3. Employment forecast by occupation ................................................................................ 21

4. Unemployment rate forecast ............................................................................................25


4

Medium to Long-term Labor Supply-Demand ForecastIntroduction and Method

Chapter 1


5

ForeCasting proCedure and Method

signiFiCanCe oF Labor suppLy-deMand ForeCasting1

2

Labor supply-demand forecasting acts as a signal

that prevents and alleviates likely imbalances in

the labor market. One type of an imbalance in

the labor market is labor force with a university

degree is unable to find suitable employment

opportunities for an extended period of

time. The main reason for such a situation is

asymmetric employment information between

labor providers and employers. In this case,

the supply-demand forecast acts as a signal

that contributes to the efficient development

and allocation of national human resources. In

general, the forecast performs both a policy

function and an information function. The policy

function: the forecast acts as the main data for

the government policies on employment, industry

and education (human resources development).

The information function: the data provided

by the forecast is used for decision making

on career or occupation selection. Through its

information function, the forecast assists the

labor market entrants to reach rational decisions

which improve the efficiency of the labor

market.

In this respect, a need to develop a labor market

projection system for Mongolia has arisen. The

development of this system has been initiated

by the Institute of Labor Studies of the Ministry

of Labor and the first pilot model of the labor

market and its results are presented in this report.

On the pilot model, two consultancy teams have

participated as well. The national consultant is a

team of economists from Gerege Partners LLC

the main role of which was to carry out the

model simulations. The international consultant

is a team of labor market experts of HRD Korea

advised on the model development.

The medium to long-term forecast consists of

the following two parts:

§ labor supply forecasting (labor force

forecasting)

§ labor demand forecasting (employment

forecasting).

Figure 1-1 shows the sequence of steps to carry

out the medium to long-term forecast. This

is the simplified version of the Korean labor

supply-demand forecasting system.

1 The Korean model is the adaptation of the US Bureau of Labor Statistics model.


6

Working age population forecasting GDP by industries

Employment coefficient forecasting (by industries)

Labor force participation rate forecasting

Economically active population forecasting (Labor supply)

Employment forecasting by industries and in aggregate (Labor demand)

Labor supply-demand forecasting “Industry-occupation” matrix forecasting

Figure 1-1. Medium to long-term labor market forecasting system

Based on the population forecast, the labor

supply forecasting initially projects 1) the

working age population (15 and older), 2)

the labor force participation rate, and 3) the

economically active population. In particular, the

working age population and the economically

active population are determined by age (age

strata in five-year increments) and gender

(male, female). The forecast period is 10 years.

The employment forecasting calculates 1) the

employment size in aggregate and by industries

by using projected industry growth rates and

the employment coefficients (the inverse of

labor productivity) by industries. Next, 2)

the employment by industries is converted to

employment by occupations using the forecast

of the industry-occupation matrix. Finally, 3) the

labor force forecast and employment forecast

results are used to calculate the economy’s total

unemployment rate and employment rate. The

employment forecast is disaggregated by 19

industries as well as by 10 major occupational

groups of National Statistical Office (NSO)

of Mongolia. The forecast period for the

employment is 10 years, the same as that for the

labor force forecast.


7

statistiCaL data used For ForeCasting

Basic statistical data used for the forecasting

includes the International Monetary Fund

(IMF)’s GDP projections for Mongolia, the NSO’s

population growth projection, the NSO’s labor

force survey and the NSO’s GDP by industries

(for a detailed description, refer to Table 1-1).

The NSO’s population growth projections, in

particular, the Medium Fertility Scenario (2B) is

used for the labor supply forecast. The working

age population is the total number of people

who are aged 15 years of age and over and

is determined by using the NSO’s labor force

survey (LFS). The economically active population

is also derived from the LFS and is the sum of

employed and unemployed population.

The IMF’s GDP projections, the share of each

industry’s GDP in the country’s aggregate GDP in

the NSO’s statistical reports and the data on the

number of employees in each industry in the LFS

reports are used for the employment forecast.

As mentioned above, the pilot model for the

medium to long-term labor supply-demand

forecast of Mongolia has been developed through

this project. From the experience of the Korean

labor market studies, the extension of this model

is possible as well as required. For example, the

employment forecast by sub-industries and sub-

occupational groups will generate more detailed

information. Also, by determining labor supply

by each occupational group and forecasting

the labor market for each occupational group,

the entrants in the labor market and school

leavers will have an opportunity to choose their

occupations rationally.

3

4 work required to be undertaken Further

Table 1-1. Statistical data used for the forecasting

Indicators Source Prepared by CommentPopulation projection Renewed population growth

projection /2010-2040/NSO by age and gender

Working age population Labor force survey NSO by age and gender

Economically active population Labor force survey NSO by age and gender

GDP by industries National income NSO by main industries

GDP projections IMF in total

Employment by industries Labor force survey NSO by main industries

Employment by occupations Labor force survey NSO ҮАМАТ-08 /ISCO-08/

by major groups


8

Major Results of the 2013-2022Medium to Long-term Forecast

Chapter 2


9

We forecast the labor force (or the economically

active population) of Mongolia until 2022 by

using the historical data on the economically

active population and the working age (15 and

older) population and labor force participation

rates.

A. Working age population forecast

The annual “labor force survey” (LFS) reports

the actual working age population who are 15

years of age and older. However LFS does not

forecast the working age population. To forecast

the working age population, we use the NSO’s

population growth projection 2010-2040. The

projection is based on “Population and Housing

Census - 2010” and has six scenarios for each

age group because of different projections of

fertility rate, mortality rate and net migration.

The projected 15 and older population until 2022

from the Medium Fertility Scenario or 2B – the

most suitable scenario of the population growth

projections - has been used in this study. The

projected 15 and older population from the NSO’s

projected population growth could not be taken

and used straight away due to methodological

difference of the LFS - the size of the working

age population in the LFS tends to be smaller

than the population of 15 and older reported

in the statistical yearbooks. Therefore, it was

required to adjust the forecast of the 15 and

older population until 2022 by forecasting this

difference.

1 Labor ForCe ForeCast

Population Trend and Projection

(by age, 15 and older)

Participation Rate Projection

Economically Active Population (Labor Force)

Projection

The labor force (or labor supply) forecast has been carried out in accordance with the following

three steps.

Figure 2-1. Process for aggregate labor supply forecast


10

Figure 2-2. Projected 15+ population (by gender, age groups, 1000 people, 2000-2022)

65+

60-64

55-59

50-54

45-49

40-44

35-39

30-34

25-29

20-24

15-19

65+

60-64

55-59

50-54

45-49

40-44

35-39

30-34

25-29

20-24

15-19

65+

60-64

55-59

50-54

45-49

40-44

35-39

30-34

25-29

20-24

15-19

65+

60-64

55-59

50-54

45-49

40-44

35-39

30-34

25-29

20-24

15-19

Male Male

Male Male

Female Female

Female Female

150 50 50 150

150 50 50 150 150 50 50 150

150 50 50 150

* Source: “Annual Population Employment Reports” submitted by aimags and UB offices of NSO.** Source: NSO’s labor force survey *** Projections

2000*

2017*** 2022***

2012**


11

The age group of 30-54 years, which has the

highest employment rate, is forecasted to

increase by 2.3 percent in the first half and by

2.2 in the second half of the projected period.

This group will be expanded by 21,900 people

annually in the period of 2012-2022.

Table 2-1 shows that the 15-64 population will

have a roughly constant share of 93-94 percent

in the total population in 2007-2022. The share

of young people of 15-29 years of age in the

total population has been declining constantly

in the last ten years and this trend is likely to

continue until 2022.

Table 2-2 shows the 15 and older population by

gender. It is evident that the share of women

is much higher compared to men and this

trend is likely to continue in the next ten years.

Approximately 48 percent of the population of

this age group is men and 52 percent is women.

In the first five years, it is estimated that the

number of men will increase by 2.1 percent but

decline to 1.4 percent annually in the last five

years of the projected period. In contrast, the

increase in numbers of women will be relatively

steady around 1.6 percent.

Table 2-1. Projected 15+ population (by age groups, 2002-2022) (unit: 1000 people, %)

Total15-29 30-54 55+

15+ 15-64

Population(1000)

2007 1632 1529 664 758 2102012 1812 1700 670 881 2612017 1982 1872 693 989 3012022 2139 1993 642 1100 397

(%) 2007 100.0 93.7 40.7 46.4 12.92012 100.0 93.8 36.9 48.6 14.42017 100.0 94.5 35.0 49.9 15.22022 100.0 93.2 30.0 51.4 18.5

Growth/Decline(1000)

‘07-’12 180 171 6 123 52‘12-’17 169 173 23 108 39‘17-’22 157 121 -51 111 96

Annual average growth rate (%)

‘07-’12 2.1 2.1 0.2 3.1 4.5‘12-’17 1.8 2.0 0.7 2.3 2.8‘17-’22 1.5 1.3 -1.5 2.2 5.7


12

B. Labor force participation rate forecast

The labor force participation rate is determined

by the ratio of the economically active population

to the working age (15 and older) population.

Based on the data of labor force participation

rate for 2006 to 2012, we forecast the labor

force participation rate by gender and age

groups until 2022 (Table 2-3).

From Table 2-3, one can see that the general

labor force participation rate which was 63.5

percent in 2012 will increase slightly to 63.7

percent in 2017 and will decline to 62.5 percent

in 2022. With respect to age groups, the labor

force participation rate has the biggest decline in

the age group of 15-29 which may be linked to

the desire to attain education. The participation

rate is the highest in the age group of 30-49

– over 80 percent. However, disaggregation

by gender shows that men’s participation rate

is the highest between 25-49 years of age

while for women it occurs later between 30-

49 years of age. Men’s labor force participation

rate will increase by 1.4 percent until 2017 and

thereafter it will decline. Meanwhile women’s

labor participation rate will decline between 15-

44 years of age. However, with the family life

becoming relatively stable between the ages of

45-54, women’s labor force participation rate

will increase.

Table 2-2. Projected 15+ population (by gender, 2002-2022) (unit: 1000 people, %)

Total Male FemalePopulation(1000)

2007 1632 786 8462012 1812 870 9422017 1983 965 10182022 2139 1036 1103

(%) 2007 100.0 48.2 51.82012 100.0 48.0 52.02017 100.0 48.7 51.32022 100.0 48.4 51.6

Growth/Decline(1000)

‘07-’12 180 84 96‘12-’17 170 95 75‘17-’22 156 71 86

Annual average growth rate (%)

‘07-’12 2.1 2.1 2.2‘12-’17 1.8 2.1 1.6‘17-’22 1.5 1.4 1.6


13

Table 2-3. Labor force participation rate forecast (by gender, age groups, 2000-2022)

Participation rate (%) Change

2000* 2012 2017p 2022p 2012-

2017p2017p-2022p

2012-2022p

Total

Total 62.9 63.5 63.7 62.5 0.1 -1.1 -1.0

15~19 44.9 27.9 21.2 22.2 -6.7 1.0 -5.7

20~24 58.4 53.7 50.9 49.9 -2.9 -1.0 -3.9

25~29 65.6 77.3 75.8 75.2 -1.5 -0.6 -2.1

30~34 70.4 81.4 80.7 80.2 -0.7 -0.6 -1.3

35~39 67.7 85.4 85.4 85.5 0.0 0.1 0.2

40~44 68.8 86.0 86.0 85.8 0.0 -0.2 -0.2

45~49 64.6 82.1 83.1 83.4 1.0 0.2 1.3

50~54 59.0 71.4 73.4 74.3 2.0 0.9 2.9

55~59 76.9 49.2 49.4 49.2 0.1 -0.2 0.0

60~64 25.7 25.8 24.7 0.1 -1.1 -1.0

65+ 15.1 12.5 12.1 -2.6 -0.4 -3.0

Male

Total 64.8 69.0 70.5 69.6 1.4 -0.9 0.5

15~19 47.6 30.7 25.0 26.6 -5.7 1.5 -4.2

20~24 61.6 60.4 58.9 58.2 -1.5 -0.7 -2.2

25~29 67.3 86.3 84.9 84.6 -1.4 -0.2 -1.7

30~34 73.1 88.4 88.3 88.1 -0.1 -0.1 -0.2

35~39 69.7 89.9 90.0 90.1 0.0 0.1 0.1

40~44 69.3 87.6 88.5 88.1 0.9 -0.4 0.5

45~49 62.5 83.7 85.8 86.1 2.1 0.2 2.4

50~54 61.7 77.3 78.9 79.2 1.5 0.3 1.9

55~59 62.3 62.7 63.0 62.3 0.3 -0.7 -0.4

60~64 33.7 33.0 32.0 -0.7 -1.0 -1.7

65+ 18.9 17.7 17.4 -1.1 -0.3 -1.5


14

C. Economically active population forecast

The forecasts of the 15 and older population and

labor force participation rate are used for the

estimation of the economically active population

forecast by age group and gender (Table 2-4),

which determines the total labor supply.

Table 2-4 shows that while the economically

active population was 1,151 thousand in 2012 it

will increase by 186 thousand people reaching

1,337 thousand in 2022. By gender, the number

of men is higher than women and this trend is

likely to continue in the next 10 years. In the

last five years the annual average growth rate

of the male labor force was 3.2 percent, this

number is forecasted to decline to 2.5 percent in

the first half of the projected period and drop

further to 1.2 percent in the second half of the

projected period. This latter reduction is asso-

ciated with both the reduction of men’s labor

force participation rate in the final five years of

the projected period (2018-2022) and the steep

decline in the number of men of 15 years of age

and over in the same period. Women’s annual

average growth rate is relatively stable around

1.1-1.2 percent over the projected period.

Female

Total 61.0 58.4 57.2 55.9 -1.2 -1.3 -2.5

15~19 42.3 25.0 17.3 17.8 -7.8 0.5 -7.3

20~24 55.4 46.7 42.7 41.3 -4.0 -1.4 -5.4

25~29 63.9 68.8 66.7 65.7 -2.1 -1.0 -3.1

30~34 67.8 74.9 73.2 72.2 -1.6 -1.0 -2.7

35~39 65.8 81.4 80.9 81.1 -0.5 0.2 -0.3

40~44 68.2 84.5 83.6 83.6 -0.9 -0.1 -1.0

45~49 66.7 80.6 80.6 80.8 0.0 0.2 0.2

50~54 56.6 66.5 68.5 69.9 2.0 1.4 3.5

55~59 38.5 37.6 37.9 -0.9 0.2 -0.6

60~64 18.8 20.0 18.9 1.2 -1.1 0.1

65+ 12.2 9.0 8.7 -3.2 -0.3 -3.5

* Source: Annual population employment report (NSO)


15

Table 2-4. Economically active population forecast (by gender, 1000 people, 2002-2022)

Total Male FemaleEconomically active population (1000)

2002* 901 454 4472007 991 514 4772012 1151 601 5512017 1262 680 5822022 1337 720 617

(%) 2002 100.0 50.4 49.62007 100.0 51.9 48.12012 100.0 52.2 47.82017 100.0 53.9 46.12022 100.0 53.9 46.1

Growth/Decline (1000)

‘03-’07 89 59 30‘08-’12 161 87 74‘13-’17 111 80 31‘18-’22 75 40 35

Annual average growth rate

‘03-’07 1.9 2.5 1.3‘08-’12 3.0 3.2 2.9‘13-’17 1.9 2.5 1.1‘18-’22 1.2 1.2 1.2

* Annual Population Employment Report (NSO)

Table 2-5. Economically active population (by age, 1000 people, 2007-2022)

Total (15 and older)15-29 30-54 55 and over

15+ 15-64Economi-cally active population (1000)

2007 990 974 317 614 592012 1151 1134 354 721 762017 1262 1248 359 813 902022 1337 1320 318 905 114

(%) 2007 100.0 98.3 32.0 62.0 6.02012 100.0 98.5 30.7 62.6 6.62017 100.0 98.9 28.4 64.4 7.22022 100.0 98.7 23.8 67.7 8.5

Growth/ Decline (1000)

‘07-’12 161 160 37 107 17‘12-’17 111 114 5 92 14‘17-’22 75 71 -41 92 24

Annual average growth rate

‘07-’12 3.0 3.1 2.2 3.3 5.2‘12-’17 1.9 1.9 0.3 2.4 3.5‘17-’22 1.2 1.1 -2.4 2.2 4.7


16

In order to forecast the labor demand, we project

the value added of each of 19 industries of the

Mongolian economy as well as the employment

coefficient (the inverse of labor productivity) of

each industry.

A. Industry value added forecast

In Mongolia, there is no medium to long-term

forecast for GDP by industries. The reason could

be that it depends on many factors and putting

them together requires complicated techniques.

In this study, we simply extrapolate the observed

share of each industry’s value added in the

aggregate GDP by using data for 2000 to 2012.

Next, we adjust IMF’s projection for Mongolian

GDP*2.

2 According to the IMF, the unemployment rate in Mongolia would decrease continuously and reach 3 percent by 2018 (source: World Economic Outlook (October 2013)). We think that it is debatable to consider it as the long-term (natural) rate of unemployment. Instead, we assume that the natural rate of unemployment is about 6 percent.

The economically active population forecast

by age groups is shown in the Table 2-5. The

population aged 15-29 was 354 thousand in 2012

and is forecasted to increase to 359 thousand

in 2017 but decline to 318 thousand in 2022.

While in the first half of the projected period

the annual average growth rate of this age

group is 0.3 percent, in the second half it will

have a sharp decline and drop to -2.4 percent.

However, the population aged 30-54, which

forms the significant portion of the economically

active population, is forecasted to grow but with

a diminishing rate. The annual average growth

rate of the population aged 55 and over, that

has the smallest share in the economically active

population, is likely to increase.

2* To forecast GDP by industries, we first used IMF’s projections of Mongolian GDP until 2018 carried out in October 2012. However, we found that with these projections, the unem-ployment rate is likely to be lower than its as-sumed long-term (natural) rate of 6 percent. Other things being equal (such as the trend of foreign labor import), it means overheating in the labor market hence could have an adverse impact on the growth rate by increasing the wage rate to adjust to the long-term equilibri-um. For this reason, we revise down the IMF’s GDP projections in our forecasting.

eMpLoyMent ForeCast by industries


17

We forecast that real GDP growth 7.1 percent

until 2017 and 6.6 percent for 2018 to 20223. In

the next five years, industries will experience the

highest growth rates are mining and quarrying

(I2), transportation and storage (I8), information

and communication (I10). In the final five years,

however, the growth rate of these industries

tend to decline (see Table 2-6).

3 According to the IMF’s projections, the average GDP growth is 8.5 percent until 2017 and 7.7 percent for 2018 to 2022.

Table 2-6. Real GDP by industries (million MNT, at 2005 constant prices)

Industries* 2007 2012 2017p 2022p

Growth (%)

2007-2012

2012- 2017p

2017p- 2022p

2012- 2022p

I1 732,275 807,208 947,449 1,170,091 2.0 3.3 4.3 3.8

I2 691,862 976,400 1,579,082 2,127,438 7.1 10.1 6.1 8.1

I3 328,067 383,449 637,422 846,806 3.2 10.7 5.8 8.2

I4 84,994 104,469 141,928 172,519 4.2 6.3 4.0 5.1

I5 18,459 22,676 32,969 42,854 4.2 7.8 5.4 6.6

I6 118,078 194,570 226,370 312,802 0.5 3.1 6.7 4.9

I7 534,378 1,199,157 1,504,011 2,109,736 17.5 4.6 7.0 5.8

I8 361,745 576,071 941,601 1,333,769 9.8 10.3 7.2 8.8

I9 28,998 64,930 69,752 96,008 17.5 1.4 6.6 4.0

I10 149,735 240,099 394,010 556,910 9.9 10.4 7.2 8.8

I11 128,635 280,834 347,503 491,645 16.9 4.4 7.2 5.8

I12 167,681 222,886 331,329 423,442 5.9 8.3 5.0 6.6

I13 18,470 63,400 76,357 110,696 28.0 3.8 7.7 5.7

I14 43,622 100,195 145,685 209,313 18.1 7.8 7.5 7.6

I15 69,847 75,198 107,878 127,897 1.5 7.5 3.5 5.5

I16 89,203 101,097 111,978 106,312 2.5 2.1 -1.0 0.5

I17 45,480 45,265 74,587 92,952 -0.1 10.5 4.5 7.5

I18 9,896 13,447 20,910 28,495 6.3 9.2 6.4 7.8

I19 18,561 27,130 40,121 54,397 7.9 8.1 6.3 7.2

Total 3,639,988 5,498,482 7,730,943 10,414,084 8.6 7.1 6.1 6.6

* see Annex for the meaning of the abbreviations.


18

B. Employment coefficient forecast

The employment coefficient is an indicator

measuring the required employment or the

number of workers to produce value added

worth 1 million MNT. In other words, this is the

inverse of labor productivity. Data on the value

added and employment of all 19 industries of the

economy for 2000 to 2012 are used to forecast

this coefficient at an industry level.

C. Employment forecast by industries

The total number of employees was 1.05 million

in 2012 and it is forecasted to increase to 1.18

million in 2017 and further by 205,446 to 1.26

million in 2022. The annual average growth rate

of employment is forecasted to be 2.3 percent

in 2012-2017 but decline to 1.3 percent in 2017-

2022. In the entire projected period (2012-

2022), the total employment tends to increase

on average by 1.8 percent or 20,545 employees

annually.

The forecast indicates that employment in the

Agriculture, Forestry and Fishing Sector (I1)

will decline by 51,706 employees by 2022. The

employment in the Construction Sector (I6)

is likely to increase with a relatively constant

annual average growth rate of 6 percent. The

Arts, Entertainment and Recreation Sector (I18)

has the highest annual growth rate of 12.3

percent in the first five years. Compared to this,

the employment in the Other Services Activities

Sector (I19) will have a slight annual growth in

the next 2 years but decline on average by 3.1

percent annually until 2022.

The employment in sectors such as Mining and

Quarrying (I2), Water Supply, Sewerage, Waste

Management and Remediation Activities (I5),

Professional, Scientific and Technical Activities

(I13), Public Administration and Defence,

Compulsory Social Security (I15), Human Health

and Social Work Activities (I17) are projected to

have a relatively high annual average growth rate

of 5-8 percent by 2022. Figure 2-3 compared

the weight of each sector’s employment in total

employment in 2012 and 2022.


19

Table 2-7. Employment forecast by industries (persons, 2012-2022, %)

Sectors 2012 2017p 2022p

Change Growth (%)

2012- 2017p

2017p- 2022p

2012- 2022p

2012- 2017p

2017p- 2022p

2012- 2022p

I1 369,960 330,890 318,254 -39,070 -12,636 -51,706 -2.2 -0.8 -1.5

I2 46,696 71,848 91,480 25,152 19,632 44,784 9.0 4.9 7.0

I3 64,897 81,600 88,754 16,703 7,154 23,857 4.7 1.7 3.2

I4 14,497 15,546 16,265 1,050 719 1,768 1.4 0.9 1.2

I5 6,681 9,891 12,856 3,210 2,965 6,175 8.2 5.4 6.8

I6 59,204 79,230 109,481 20,025 30,251 50,276 6.0 6.7 6.3

I7 131,340 147,710 128,148 16,370 -19,562 -3,192 2.4 -2.8 -0.2

I8 56,091 65,704 65,585 9,613 -119 9,494 3.2 0.0 1.6

I9 30,235 31,986 38,341 1,751 6,355 8,106 1.1 3.7 2.4

I10 14,740 19,262 23,433 4,522 4,171 8,693 5.5 4.0 4.7

I11 17,376 21,832 22,882 4,456 1,050 5,506 4.7 0.9 2.8

I12 1,208 1,301 1,659 93 358 451 1.5 5.0 3.2

I13 11,341 17,036 24,734 5,695 7,698 13,393 8.5 7.7 8.1

I14 13,334 14,483 11,772 1,150 -2,711 -1,562 1.7 -4.1 -1.2

I15* 62,919 89,184 108,962 26,265 19,779 46,043 7.2 4.1 5.6

I16 86,269 95,865 94,793 9,596 -1,072 8,524 2.1 -0.2 0.9

I17 37,529 59,184 73,829 21,655 14,645 36,300 9.5 4.5 7.0

I18 7,357 13,123 16,181 5,766 3,058 8,824 12.3 4.3 8.2

I19 19,783 18,507 14,477 -1,276 -4,030 -5,306 -1.3 -4.8 -3.1

Total 1,051,4571 1,184,181 1,261,886 127,740 77,705 205,446 2.3 1.3 1.8

* I15 represents “Public administration and defence; compulsory social security”. The increase projected in the number of employees in this industry reflects the historical pattern only in a sense that it does not reflect policies that the government intends to implement such as the “From the bureaucratic government to a smart govern-ment” manifesto.


20

It can be seen that 35 percent of employees of

15 and older were employed by the Agriculture,

Forestry and Fisheries (I1) in 2012 tends to de-

cline to 25.2 percent by 2022. Also the employ-

ment share in the sectors such as Wholesale and

Retail Trade, Repair Motor Vehicle and Motor-

cycles (I7), Administrative and Support Service

Activities (I14), Education (I16) and Other Service

Activities (I19) is likely to lower in 2022 com-

pared to 2012. In contrast, the shares of other

sectors are likely to increase.

Figure 2-3. Observed and forecasted employment by industries (%)

Other service activities

Arts, entertainment and rec

Human health and social work activities

Education

Public administration and defence;..

Administrative and support service activitie

Professional, scientific and technical activities

Real estate activities

Financial and insurance

Information, communication

Accommodation and food service activitie

Transportation and storage

Wholesale and retail trade, repair of motor..

Construction

Water supply, sewerage, waste..

Electricity, gas, steam and air conditioning..

Manufacturing

Mining and quarring

Agriculture, Forestry, Fishing and Hunting

0 10 20 30 40

2022p

2012*


21

eMpLoyMent ForeCast by oCCupation

In Mongolia, ISCO-08 occupational classification

groups are used and we carry out the

employment forecast for 2013 to 2022 for

each of the ten major groups (1-digit). In doing

so, we use the “industry-occupation” matrices

for 2007 to 2012. This matrix divides the total

employment size in a given year into industries

and occupational groups. For each industry,

by extrapolating the observed share of the

employment in each occupational group in the

total industry employment, we forecast the

“industry-occupation” matrix for 2013 to 2022

(see Tables 2-9, 2-10). Summing up across the

industries, we derive the total (economy-wide)

employment size in each occupational group

(Table 2-8).

For the period of 2012-2022, the fastest growing

occupations are М1 (manager), М3 (technicians

and associated professionals), М7 (craft and

related trades workers) and М9 (elementary

occupation)4. The average growth of the

employment in these occupations is over 4

percent. On the other hand, the demand for M6

(skilled agriculture, forestry, and fishery workers)

3

Table 2-8. Employment forecast by 10 major occupational groups (number, %)

Major occupational groups 2007-08* 2012* 2017p 2022p

Growth (%)

2012-2017p

2017p-2022p

2012-2022p

M1 41,646 58,429 76,423 87,788 5.5 2.8 4.2

M2 114,433 161,560 196,699 227,045 4.0 2.9 3.5

M3 44,044 37,069 52,135 57,916 7.1 2.1 4.6

M4 16,840 27,064 30,022 34,177 2.1 2.6 2.4

M5 110,567 162,105 177,769 173,289 1.9 -0.5 0.7

M6 363,511 362,750 319,927 306,790 -2.5 -0.8 -1.7

M7 90,479 93,241 127,043 145,660 6.4 2.8 4.6

M8 70,029 78,240 101,578 110,298 5.4 1.7 3.5

M9 48,254 70,734 96,987 112,027 6.5 2.9 4.7

M10 5,250 5,600 6,897 1.3 4.3 2.8

Total 899,802 1,056,441 1,184,181 1,261,886 2.3 1.3 1.8

* NSO’s labor force survey /only domestic workers/p Projected results /the sum of domestic and foreign workers/

4 М2 is for professionals, М4 is for clerical support workers, М5 is for service and sales workers, М8 is for plant and machine operators and assemblers.


22

Figure 2-4. Observed and projected employment by 10 major occupational groups (%)

Below we show the projected “industry-occupation” matrices as of 2017 and 2022.

2022p 2012*

M10

M9

M8

M7

M6

M5

M4

M3

M2

M1

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

tends to decrease. The decrease in M6 tends to

contribute to the increase in employment in the

most occupational groups.

The following figure compares the observed

share of the employment in each occupational

group in the total employment in 2012 with its

projected in 2022. In 2012, М6 (skilled agriculture,

forestry, and fishery workers) accounted for

34.3 percent of the total employment while in

2022, it tends to account for 24.3 percent. The

share of М10 (armed force occupation) tends

to remain roughly the same around 0.5 percent.


23

Tabl

e 2-

9. “

Indu

stry

-occ

upat

ion”

mat

rix (

num

ber, 2

017)

IndustriesO

ccup

atio

nal g

roup

s

M

1M

2M

3M

4M

5M

6M

7M

8M

9M

10To

tal

I117

9113

8298

024

717

2631

7120

2177

2303

3165

33

0890

I257

3795

5328

7017

3339

6311

885

1921

333

1802

271

848

I379

8581

5917

2814

3648

2156

844

155

5906

6844

8160

0

I482

044

6712

4850

125

40

4385

1813

2058

1554

6

I540

576

261

855

475

60

2021

1337

3437

9891

I664

0012

032

2615

1617

2135

216

4188

441

6981

6279

230

I794

7872

8034

3325

3510

2563

435

1019

131

2786

6714

7710

I817

4326

6813

7233

0731

3710

719

8847

387

3995

6570

4

I969

0786

545

010

3617

587

8730

559

141

5831

986

I10

3887

8756

2282

1501

930

090

431

468

819

262

I11

3151

7731

2328

5238

1208

020

510

0896

421

832

I12

532

443

326

--

--

--

1301

I13

1102

9875

3848

231

460

168

211

334

807

1703

6

I14

2906

2156

610

739

3875

159

995

880

2164

1448

3

I15

1569

024

102

1170

149

8010

073

386

1478

6949

8313

5511

8918

4

I16

3783

5817

633

3727

3592

3020

815

6814

3015

309

8895

865

I17

1140

3140

410

228

910

5945

146

1497

2290

5624

5918

4

I18

1580

5093

712

313

1477

121

764

232

2831

1312

3

I19

1386

1797

1450

409

7630

8837

9517

417

7818

507

To

tal

7642

319

6699

5213

530

022

1777

6931

9927

1270

4310

1578

9698

756

00


24

Tabl

e 2-

10. “

Indu

stry

-occ

upat

ion”

mat

rix (

num

ber, 2

022)

IndustriesO

ccupat

ional

gro

ups

M

1M

2M

3M

4M

5M

6M

7M

8M

9M

10To

tal

I119

3215

04

1052

258

1808

3038

69

2279

2382

3170

31

825

4

I274

5011

659

3603

2324

4940

102

1014

827

953

2330

191

480

I3874

09425

1926

1692

5647

494

470

44

625

175

348875

4

I4875

5059

1228

454

211

0435

215

7025

1716

265

I550

8963

875

808

1032

027

84

1694

419

212

856

I6920

917

428

3637

2494

2996

281

57622

5030

1078

310

9481

I777

39659

628

85

2243

9051

937

9820

327

146868

12814

8

I817

88

2678

1340

3569

3030

110

1913

472

3639

2065

585

I98959

998

598

1294

2029

310

418

274

251

7038

341

I10

5112

1129

426

2116

2611

510

847

89

693

23433

I11

3351

7567

2271

6047

1235

021

711

7210

2222

882

I12

668

545

447

--

--

--

1659

I13

1393

14486

5810

239

688

249

150

445

1273

2473

4

I14

2422

1603

489

682

3023

128

860

748

1816

1177

2

I15

19636

30494

12886

616

511

414

477

1670

850

710

903

6809

10896

2

I16

3728

57459

2156

2699

9638

209

1583

1003

1623

187

9479

3

I17

1201

3978

412

041

1055

7465

183

2015

2557

7528

73829

I18

2033

611

3861

190

1921

145

901

163

3853

1618

1

I19

1045

1389

1191

337

627

659

2889

40

1250

14477

To

tal

877

88

2270

45

5791

634

177

1732

89

3067

9014

5660

1102

9811

2027

6897


25

uneMpLoyMent rate ForeCast

We derive the unemployment rate forecast by

using the labor force (labor supply) forecast and

the employment (labor demand) forecast.

In 2012, the unemployment rate was 8.2

percent and we assume that the long-term

unemployment rate is around 6 percent (± 0.5

percentage points) to derive the results in the

forecasting model. In other words, we assume

that the natural (or structural, NAIRU) rate of

unemployment is about 6 percent. We revise

down the growth of GDP projected by IMF and

derive the labor demand such that the economy

will experience the natural rate of unemployment

in the long-term.

4

Table 2-11. Unemployment rate forecast (number, %, 2012-2022)

Labor demand Labor supply Unemployment rate (%)

2012* 1,056,441 1,151,146 8.2

2013 1,110,160 1,180,712 6.0

2014 1,137,663 1,203,672 5.5

2015 1,150,724 1,224,913 6.1

2016 1,168,275 1,244,381 6.1

2017 1,184,181 1,262,139 6.2

2018 1,198,089 1,278,435 6.3

2019 1,211,819 1,293,652 6.3

2020 1,229,756 1,308,260 6.0

2021 1,244,758 1,322,684 5.9

2022 1,261,886 1,337,189 5.6

* Source: NSO’s labor force survey


26

annex: abbreviated words

I1

I2

I3

I4

I5

I6

I7

I8

I9

I10

I11

I12

I13

I14

I15

I16

I17

I18

I19

М1

М2

М3

М4

М5

М6

М7

М8

М9

М10

Agriculture, Forestry, Fishing and Hunting

Mining and quarrying

Manufacturing

Electricity, gas, steam and air conditioning supply

Water supply, sewerage, waste management and remediation activities

Construction

Wholesale and retail trade, repair of motor vehicles and motorcycles

Transportation and storage

Accommodation and food service activitie

Information, communication

Financial and insurance activities

Real estate activities

Professional, scientific and technical activities

Administrative and support service activities

Public administration and defence; compulsory social security

Education

Human health and social work activities

Arts, entertainment and recreation

Other service activities

Manager

Professionals

Technicians and associate professionals

Clerical support workers

Service and sales workers

Skilled agriculture, forestry and fishery workers

Craft and related trades workers

Plant and machine operators and assemblers

Elementary occupation

Armed forces occupation