Chapter 4. Migration and the labour market in Georgia

Emigration can affect many aspects of the labour market – from wage levels and the supply of labour to the promotion of self-employment. High levels of unemployment and a poorly functioning labour market are key drivers of emigration in Georgia. Using survey data, this chapter investigates which segments of the workforce are most likely to emigrate, and the impact of emigration and remittances on employment and self-employment among both those who are left behind and those who return. It explores how government efforts to develop employment agencies, vocational training, and public employment programmes have benefited households and affected migration decisions.

  

Georgia’s main labour market challenges today are significant levels of unemployment, mismatches between market demand and skills supply, and informal employment, all of which encourage people to emigrate. Emigration can affect wage levels and unemployment by reducing the labour supply nationally and within households – the overall result can be to constrain productivity and development. Remittances may allow households to leave paid employment or start up a small business. Return migrants bring financial, human and social capital accumulated abroad back to their country, and may also start new businesses, creating new jobs in their country of origin. At the same time, labour market policies aiming to generate new jobs and match labour supply and demand can affect patterns of migration.

This chapter explores the relationships between migration and the labour market in Georgia. It begins with an overview of the labour market, before analysing how the various migration dimensions affect key labour market outcomes, such as the work choices of migrant households and individuals. It then examines the influence of labour market policies and programmes on individuals’ migration decisions. The chapter concludes with policy recommendations from the findings.

A brief overview of the labour market in Georgia

According to 2015 data from the National Statistics Office of Georgia (GeoStat), the country’s labour-force participation rate, defined as the ratio of people in the labour force to the population aged 15 and older, was 68%, compared to an employment rate of 60%.1 The labour-force participation rate, according to GeoStat is notably higher in rural than urban areas (75% versus 60%), and among men than women (78% versus 59%), mostly because being a housewife and hence out of the labour force is quite common in Georgia. Similarly, the employment rate is also significantly higher in rural than urban areas (72% versus 47%) and among men than women (68% versus 53%). Self-employment is traditionally prevalent, accounting for 57% of the employed population. Self-employment is markedly higher in rural than urban areas (76% versus 27%), which most likely reflects the fact that Georgia is traditionally an agricultural country.

The overall unemployment rate in the country is 12%2 (GeoStat), although this is likely to be an underestimate – according to the World Bank (2013) estimates, if “discouraged workers”3 were not excluded from the labour force, the unemployment rate would have been several percent points higher. There are notable differences in employment rates by geographical location, with 5% unemployment in rural areas, compared to 22% in urban settlements. Unemployment is highest among young people. From a rate of 32% and 31% respectively among 15-19 year-olds and 20-24 year-olds, it falls to 24% and 17% respectively among 25-29 year-olds and 30-34 year-olds and then further still to 16% for 35-39 year-olds and down to 1% among those over 65. The high rate of youth unemployment is a global challenge, especially in developing countries and countries in transition, and not unique to Georgia (ILO, 2015).

Most of the unemployed have either a secondary or a higher education qualification. This might reflect the fact that even highly educated workers do not have skills needed on the labour market in Georgia. Most of the jobs available do not require higher education, and highly educated workers who have less skilled jobs earn lower salaries than workers who are specialised for a particular job (World Bank, 2013). In addition, in the two largest sectors in Georgia – agriculture and trade – there is limited demand for workers with higher education.

This general national pattern is reflected in the Interrelations between Public Policies, Migration and Development (IPPMD) survey data (Table 4.1). The labour-force participation rate among the survey sample (people aged 15-64) is about 61% – 76% for men and 48% for women – and is higher in rural areas (76%) than urban areas (49%). However, the employment rate among those surveyed is significantly lower than the official statistics, at 39% (50% for men and 29% for women). As with the national figures, the rural employment rate is higher than for urban areas, a difference mainly explained by the prevalence of self-employment. Around 40% of the working population (aged 15-64) reported not being engaged in paid employment and not looking for work. The rate is higher among all individuals aged 15 and over (49%), taking the retired into account. The survey found higher levels of unemployment (22%) than the official figures. It also found that unemployment was lower among 15-24 year-olds (22%) than among 25-34 year-olds and 35-44 year-olds (28% and 29%, respectively).

Table 4.1. The Georgian IPPMD sample largely reflects the national labour market picture

All

Men

Women

Urban

Rural

Labour market characteristics (15-64)

Number of employed individuals

1 998

1 232

766

869

1 129

Number of unemployed individuals

1 145

635

510

501

644

Number of individuals

5 132

2 468

2 664

2 808

2 324

Labour-force participation rate

61%

76%

48%

49%

76%

Employment rate

39%

50%

29%

31%

49%

Employment status (15-64)*

5 117 (100%)

2 459 (100%)

2 658 (100%)

2 803 (100%)

2 314 (100%)

Self employed

717 (14%)

545 (22%)

172 (6%)

273 (10%)

444 (19%)

Paid employee in public sector

526 (10%)

229 (9%)

297 (11%)

316 (11%)

210 (9%)

Paid employee in private sector

755 (15%)

458 (19%)

297 (11%)

540 (19%)

215 (9%)

Unemployed

1 145 (22%)

635 (26%)

510 (19%)

644 (23%)

501 (22%)

Not in paid work and not looking for work

1 954 (38%)

580 (24%)

1 374 (52%)

1 018 (36%)

936 (40%)

Other

20 (0%)

12 (0%)

8 (0%)

12 (0%)

8 (0%)

Note: * shows number of observations.

Source: Authors’ own work based on IPPMD data.

How does migration affect the labour market in Georgia?

Migration can affect the labour market in various ways. With fewer people in the household available to work, the household members who are left behind may have to seek work to compensate. If the migrants send home remittances, however, this may allow their families to stop working or to set up their own business. Emigration and remittances can also affect the types of jobs chosen by the household members who are left behind. Furthermore, return migrants bring home a range of resources accumulated abroad which can also change employment patterns.

Emigrants are more likely to come from the health sector and more skilled occupations

Emigration means a reduction in a country’s population overall. It can also reduce the labour supply if the migrants were employed before emigrating. In theory, a significant drop in the labour supply can reduce competition in the labour market, which in turn increases wage levels and decreases unemployment. The effect, however, can vary depending on the characteristics of the workers who fill the jobs left open by emigrants. Wages will be higher for those whose skills can substitute for the skills of those who left, but lower for individuals whose skills complement workers who left. The effect of a fall in supply may be exacerbated in labour-intensive sectors such as agriculture.

When looking at the labour characteristics of current emigrants, the IPPMD survey finds that 60% were unemployed before leaving the country and that 97% were of working age (Table 4.2). Their unemployment rate has significantly decreased since they emigrated. This implies that unemployment is one of the biggest push factors for leaving the country.

Table 4.2. Emigration boosts employment among emigrants
Employment status of emigrants before and after emigration (%)

Employment status (%)

Before leaving

In the destination country

Total sample

Men

Women

Total sample

Men

Women

Self employed

7

8

6

14

20

10

Paid employee in the public sector

8

7

10

7

7

6

Paid employee in the private sector

9

8

9

60

54

65

Unemployed

59

59

59

6

7

4

Not in paid work and not looking for work

16

16

15

10

7

12

Other

1

2

1

4

5

2

Source: Authors’ own work based on the IPPMD data.

It is possible that some sectors are more affected by emigration than others. The IPPMD research explored this for four key sectors – agriculture, construction, education and health – comparing the number of emigrants who left each sector with the number of workers remaining (Figure 4.1, left-hand chart). The health sector seems to be the most affected by emigration. The emigration of highly skilled workers can also have a direct impact on the labour market. Exploring the patterns of emigration among occupational groups at different skills levels reveals that Georgia is losing more skilled workers to emigration (Figure 4.1, right-hand chart).

Figure 4.1. The health sector and highly skilled occupations are losing more workers to emigration
picture

Note: The skills level of occupations has been categorised using the International Standard Classification of Occupations (ISCO) provided by the International Labour Organization (ILO, 2012). Skills level 1: occupations which involve simple and routine physical or manual tasks (includes elementary occupations and some armed forces occupations). Skills level 2: clerical support workers; services and sales workers; skilled agricultural, forestry and fishery workers; craft and related trade workers; plan and machine operators and assemblers. Skills level 3: technicians and associate professionals and hospitality, retail and other services managers. Skills level 4: Other types of managers and professionals.

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933457830

Emigration and remittances particularly affect women’s employment decisions

Emigration and remittances can reduce the supply of labour among remaining household members. They may work fewer hours or leave the labour market altogether (Grigorian and Melkonyan, 2011; Kim, 2007). Remittances can decrease the motivation to work or undertake entrepreneurial activities (EPRC, 2011). Some stakeholders interviewed as part of the IPPMD study (Chapter 3) felt that although remittances provide an important source of income for Georgia’s economy and are a means of survival for many families, they could hamper economic activities because they are usually spent on primary consumption rather than on strategic long-term profit-making activities. Furthermore, they noted that living on remittances reduced people’s motivation to find jobs.

Although it is challenging to isolate the effects of having a family member who has emigrated and the receipt of remittances, the IPPMD data give some clues on this matter. Figure 4.2 compares the average share of working household members from non-migrant households, emigrant households not receiving remittances and those that are receiving remittances. The figure shows that remittance-receiving households have the lowest share of working adults. Gender patterns differ, however. While there is not much difference between the employment rate for men in remittance versus non-remittance receiving households, women in emigrant households without remittances are more likely to work than women in the other types of households.

Figure 4.2. Households receiving remittances have fewer working members
Share of household members aged 15-64 who are working (%)
picture

Note: The sample excludes households with return migrants only. Share of men that are working in emigrant households not receiving remittances are close to that of emigrant households receiving remittances.

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933457844

Regression analysis deepened the investigation into how migration is associated with household labour supply (Box 4.1). The results confirm that individuals in households receiving remittances are less likely to work (Tables 4.3 and 4.4). The receipt of remittances appears to play a stronger role on women’s employment than for men, as already suggested above. The likelihood of unemployment is increased among women receiving remittances in both rural and urban areas. The emigration of a household member seems to be negatively associated with the number of men working in a given household, especially in rural areas. This is most likely to be because of the difficulties in replacing for male labour in the household.

Box 4.1. The links between migration and employment

To investigate the link between migration and households’ labour decisions, the following regression models were used:

picture (1)

picture (2)

picture (3)

where share_workinghh signifies households’ labour supply, measured as the share of household members aged 15-64 who are working. m_share_workinghh is the share of male household members that are working among men and f_share_workinghh for female household members. emighh represents a variable with the value of 1 where a household has at least one emigrant, and remithh denotes a household that receives remittances. controlshh stands for a set of control variables at the household level.1 δr implies regional fixed effects and εi is the randomly distributed error term. The models were run for two different groups of households depending on their location (rural or urban). The coefficients of variables of interest are shown in Table 4.3.

Table 4.3. Remittances reduce households’ labour supply

Dependent variable: Share of the employed among household members aged 15-64

Main variables of interest: Having an emigrant/receiving remittances

Type of model: OLS

Sample: All households with at least one member working

Variables of interest

Share of the employed household members among:

(1) All

(2) Men

(3) Women

rural

urban

rural

urban

rural

urban

Household has at least one emigrant

-0.032 (0.031)

0.002 (0.030)

-0.131*** (0.046)

-0.022 (0.042)

0.028 (0.039)

0.013 (0.039)

Household receives remittances

-0.094*** (0.032)

-0.106*** (0.032)

-0.020 (0.047)

-0.079* (0.047)

-0.129*** (0.041)

-0.110*** (0.042)

Number of observations

830

1 050

704

851

789

981

Note: Results that are statistically significant are indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors in parentheses.

The following probit regression model was used to further investigate the link between migration and unemployment:

picture (4)

where unemployedi signifies whether an individual i is unemployed. The results are shown in Table 4.4.

Table 4.4. Women in households receiving remittances are more likely to be unemployed

Dependent variable: Individual is unemployed

Main variables of interest: Having an emigrant/receiving remittances

Type of model: Probit

Sample: Labour force among working age members (15-64)

Variables of interest

(1) All

(2) Men

(3) Women

rural

urban

rural

urban

rural

urban

Household has at least one emigrant

0.065* (0.035)

0.015 (0.032)

0.118*** (0.042)

0.024 (0.041)

-0.033 (0.063)

0.002 (0.049)

Household receives remittances

0.061* (0.036)

0.066* (0.035)

0.008 (0.043)

0.032 (0.046)

0.166*** (0.063)

0.102* (0.054)

Number of observations

1 369

1 770

892

973

477

797

Note: Results that are statistically significant are indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors in parentheses.

1. Control variables include the household’s size and its squared value, the dependency ratio (number of children 0-15 and elderly 65+ divided by the total of other members), the male-to-female adult ratio, family members’ mean education level, its wealth estimated by an indicator (Chapter 3) and its squared value.

Remittances seem to stimulate more self-employment among men

Remittances raise household income. Not only can they help meet basic consumption needs and reduce poverty (Acosta et al., 2008; Adams and Page, 2005), they can also provide those left behind with the capital they need to start up a business and boost self-employment (Mesnard, 2004; Dustmann and Kirchkamp 2002; Woodruff and Zenteno, 2007; Yang, 2008). While Chapter 7 explores how remittances affect enterprises in more detail, this section focuses on the link between remittances and self-employment. The IPPMD data find that for both men and women, the share of self-employed people is higher among households receiving remittances than those not receiving remittances (Figure 4.3).

Figure 4.3. Self-employment is higher among remittance-receiving households
Employment types among employed people, working age population (%)
picture

Note: The difference between households not receiving and receiving remittances is not statistically significant for either men or women (using a chi-squared test).

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933457852

These patterns are confirmed by regression analysis (Box 4.2). Table 4.5 shows the results of the analysis and suggests that receiving remittances is positively associated with self-employment in rural areas – although this pattern only holds for men.

Box 4.2. The links between remittances and self-employment

To further analyse how receiving remittances is associated with the employment types of the household members, a probit model was used in the following form:

picture (5)

where self_employedii represents whether an employed individual i is self-employed. remithh signifies that a household receives remittances. controlsi stands for a set of control variables at the individual level and controlshh for household level controls.1 δr implies regional fixed effects and εi is the randomly distributed error term. Table 4.5 shows the computed marginal effects of the main variable of interest on each employment type.

Table 4.5. Houesholds receiving remittances are more likely to have self-employment members in rural areas

Dependent variable: An individual is self-employed (binary variable).

Main variables of interest: The individual belongs to a household receiving remittances.

Type of model: Probit

Sample: Employed people of working age (15-64).

Variables of interest

(1) All

(2) Men

(3) Women

rural

urban

rural

urban

rural

urban

Household receives remittances

0.080** (0.037)

0.033 (0.031)

0.101** (0.047)

0.026 (0.044)

0.035 (0.061)

0.039 (0.039)

Number of observations

868

1 127

592

639

273

488

Note: Results that are statistically significant are indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors in parentheses.

1. Control variables include age, sex and education level of individuals and their households’ size and its squared value, the dependency ratio, its wealth estimated by an indicator and whether it is in a rural or urban location.

Return migration can boost self-employment in Georgia

Return migrants tend to come home with greater financial and human capital. Savings accumulated abroad can be used as a resource for working on their own account. Growing evidence from the literature suggests that return migrants tend to be self-employed or establish their own businesses (De Vreyer et al., 2010; Ammassari, 2004). Figure 4.4 compares the employment status of non-migrants and return migrants. While the share of economically non-active individuals is considerably lower for return migrants than non-migrants, return migrants are more likely to be unemployed. Looking at the employed population, return migrants are more likely to be self-employed than non-migrants.

Figure 4.4. A higher share of return migrants are self-employed than non-migrants
Employment status among adult non-migrants and return migrants (%)
picture

Note: The difference in the distribution of employment statuses between non-migrants and return migrants is statistically significant (99% significance level, using a chi-squared test).

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933457868

Could it be that return migrants were already self-employed prior to their migration or did they choose migration as a strategy to set up a business or to become self-employed? Figure 4.5 compares the employment status of return migrants before their emigration and after their return. As with current emigrants, more than half of return migrants were unemployed before emigrating. The share of unemployment decreases remarkably after their return. Some of them have left the labour market while others are employed in the private sector or are self-employed.

Figure 4.5. Return migrants are more likely to be self-employed than when they left
Employment status among return migrants before leaving and after return (%)
picture

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933457878

How do labour market policies affect migration in Georgia?

The previous section has considered how migration affects the labour market. At the same time, migration is equally affected by Georgia’s labour market policies. Effective labour market policies can have an indirect impact on households’ migration decisions. Policies to improve the domestic labour market may reduce the incentive to migrate. Such policies can seek to enhance labour market efficiency through state employment agencies, improve the skills set of the labour force through vocational training, and expand labour demand by increasing public employment programmes.

To date, the impact of these labour market policies on migration in Georgia remains unexplored in the research. This section attempts to disentangle the link between these policies and the decision to emigrate and the reintegration of return migrants into the labour market. Box 4.3 describes how the IPPMD survey covered labour market policies and programmes.

Box 4.3. Labour market policies and programmes covered in the IPPMD project

The IPPMD household survey asked household members whether they had benefited from certain labour market policies and programmes (Figure 4.6). It also asked people employed in the public and private sectors how they found their jobs, with government employment agencies being one of the options. The survey also asked the labour force if they had participated in any vocational training programmes, and if so what type of training they received. They were also asked about participation in public employment programmes.

The community survey collected information on the existence of vocational training centres and job centres. It also asked if certain types of training programmes have been held in the communities and whether they have offered public employment programmes.

Figure 4.6. Labour market policies explored in the Georgian surveys
picture

Note: The IPPMD survey also asked if individuals received unemployment benefits but this question was not included in the Georgian survey as it had no unemployment benefits at the time of the survey.

Unemployment motivates people to emigrate

Unemployment is one of the strongest incentives for emigrating from Georgia. The IPPMD data confirm that individuals are more likely to be planning to emigrate when they are unemployed (Box 4.4). Unemployment is a push factor for emigration in both rural and urban areas but with a stronger impact in urban areas. Table 4.6 takes a closer look at the data disaggregated by gender and area of residence. It suggests that unemployment is a strong push factor for all groups, although no statistically significant relationship was found for men in rural areas. Considering the role of unemployment in emigration from Georgia, labour market policies aimed at reducing unemployment will affect the migration decisions of households and individuals.

Box 4.4. The links between unemployment and emigration

To further analyse how unemployment is associated with emigration plans, a probit model was used in the following forms:

picture (6)

where plan_emigi is emigration plan of individual i. It takes a value of 1 if the individual has a plan to emigrate and 0 if not. unemployedi represents that an individual i is unemployed. controlsi stands for a set of control variables at the individual level and controlshh for household level controls.1 δr implies regional fixed effects and εi is the randomly distributed error term. The model has been tested for two different groups of households depending on their location (urban or rural). Table 4.6 shows the computed marginal effects of the main variable of interest (being unemployed) on individuals’ plans to emigrate.

Table 4.6. People are more likely to have plans to emigrate when they are unemployed

Dependent variable: Individual plans to emigrate

Main variables of interest: Individual is unemployed

Type of model: Probit

Sample: Working age (15-64) population

Variables of interest

(1) All

(2) Men

(3) Women

Rural

Urban

Rural

Urban

Rural

Urban

Unemployed

0.026** (0.011)

0.037*** (0.010)

0.022 (0.015)

0.044*** (0.015)

0.032* (0.019)

0.029** (0.014)

Number of observations

1 349

1 770

879

973

470

797

Note: Results that are statistically significant are indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors in parentheses.

1. Control variables include age, sex and education level of individuals and their households’ size and its squared value, the dependency ratio and its wealth estimated by an indicator.

Government employment agencies have a limited impact on migration in Georgia

The IPPMD survey asked how individuals had found paid jobs in the public and private sectors (Figure 4.7). The most common way to find a job is through friends and family, followed by a direct approach to potential employers. Together, these channels account for about 80% of all surveyed adults with paid jobs in both the public and private sectors. Only 5% of employed respondents had found their job via a government employment agency. There is a slight difference between rural and urban areas: people in rural areas directly approached employers more than in urban areas, whereas government employment agencies are more commonly used in urban areas.

Figure 4.7. Government agencies play a minor role in job seeking
Methods for finding a paid job in both public and private sectors
picture

Note: The difference between urban and rural areas is statistically significant (95% significance level, using a chi-squared test).

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933457883

While the share of people who benefited from government employment agencies is low, there are certain patterns related to migration. A comparative study of the ten IPPMD partner countries suggests that the share of people who have no plans to emigrate is higher for those who found jobs through government employment agencies than those who did not (OECD, 2017). However, this is not the case in Georgia. While 95% of the beneficiaries of government employment agencies have no plans to emigrate, this is lower than the share among non-beneficiaries (97%).4 This is somewhat surprising, especially given that 80% of the beneficiaries have jobs in the public sector which are usually seen as secure.

A key policy in this area is the “Social-economic Development Strategy of Georgia – Georgia 2020” (Government of Georgia, 2014). This policy led to the creation of the Department of Employment Issues within the Social Service Agency, where the Employment Support Centres will also be established. The Social Service Agency is responsible for employment services in Georgia, together with many other services. It was established in 2007 by uniting the State Agency for Social Service and Employment and the United State Fund for Social Insurance. It has approximately 70 offices located across Georgia. The agency has also initiated an online service, Worknet, where job seekers and employers can register and be connected. It also organises job forums. The agency’s territorial offices provide help to those without access to the internet or who do not have sufficient computer skills to use this service.

Vocational training programmes have little effect on migration

Vocational training programmes can affect several migration outcomes. By enhancing labour skills, people may find better jobs in the domestic labour market, thereby reducing the incentive to emigrate. On the other hand, vocational training can be a means to make would-be migrants more employable overseas. According to the comparative IPPMD study, migration intentions of employed and unemployed people who participated in vocational training are likely to be stronger than those who did not (OECD, 2017). While this is true at the descriptive level for Georgia, the difference is not statistically significant. Further analysis has found no significant relationship between vocational training programmes and households’ migration experiences.

Vocational training has become a key labour market strategy in Georgia, as in many other countries. In March 2007, the new Law of Georgia on Professional Associations was passed, significantly changing the financing and infrastructure of the vocational education system in Georgia. Vocational education in Georgia is managed by government structures, which develop national development policies and strategies and programmes. The Ministry of Education and Science enforces the regulatory framework and implements sector programmes through its agencies: the National Centre for Education Quality Enhancement, the National Centre for Teachers’ Professional Development and the Information Management System. In 2013, the government adopted the Vocational Education and Training Development Strategy for 2013-2020 (MoES 2013). For then there were 23 public and 76 private vocational education and training (VET) institutions, 25 higher educational institutions and 13 schools authorised by the government to provide vocational education programmes. In total, around 150 different vocational education programmes were taught at these institutions.

The strategy document identifies several important challenges facing vocational education in Georgia today. Vocational education is not attractive to the population and is not required as a precondition for recruitment by employers, as the quality of VET qualifications awarded are often low, and are not recognised by employers and education institutions either locally or internationally. VET educators themselves lack the capacity and professional development to meet modern standards and requirements. Both public and private VET providers lack sufficient funding, good management and up-to-date and quality equipment. Most importantly, VET programmes are often not relevant to the current and future labour needs of Georgia’s growing and diversifying economy.

Before the adoption of the strategy in 2013, vocational courses were focused on a number of key sectors, particularly construction, the hospitality sector, information technology (IT) and textiles. Sectors that employ large numbers of technical people – like utilities, rail, steel, food processing and logistics – were hardly covered by the VET system and had to provide almost all of their training in house. The strategy documented the problems such as the low quality of vocational education, the lack of professional skills of VET graduates, low awareness of VET programmes and the need to involving employers directly in the VET system.

The IPPMD survey found that about 4% of the labour force had participated in a vocational training programme in the past five years. The participation rate in vocational training programmes is higher for women than men; and higher in rural areas than in urban areas (Figure 4.8). The most common training programmes are computer and IT-related (31%), followed by languages (15%).

Figure 4.8. Women in rural areas have the highest participation rate in vocational training programmes
Share of labour force who have participated in vocational training in the past five years (%)
picture

Note: The difference between men and women in both urban and rural areas is statistically significant (99% significance level, using a chi-squared test).

Source: Authors’ own work based on IPPMD data.

 http://dx.doi.org/10.1787/888933457893

Both government employment agencies and vocational training programmes can serve as a reintegration channel for return migrants. As re-entry to the home labour market may require some return migrants to acquire new skills, training programmes can help returnees to develop these skills and find employment. However, the rate of use of such programmes by the return migrants in the survey is close to zero. Return migrants’ lack of use of government employment agencies may partially explain their propensity to self-employment. In this case, they may have chosen to be self-employed as a last resort.

Public employment programmes are too small scale to make an impact

In theory, PEPs can either increase or decrease the incentives to migrate depending on households’ response to the additional income received through such programmes. Programmes which improve local employment opportunities may reduce the incentives to migrate as the opportunity cost of migration increases. In rural areas in particular, public works programmes to support agricultural workers during the farming off-season can provide an alternative to seasonal migration. On the other hand, the increased income received may encourage migration. Overall, the impact of PEPs on migration is likely to depend on their duration, coverage and income level.

Georgia does have some public employment programmes (PEPs) in place; however, they are mostly targeted at a small number of people, such as specific groups including students, former prisoners and people with disabilities. They also only offer short-term employment. This may explain the low take-up of PEPs among the IPPMD sample in Georgia (less than 1%).

Conclusions and policy recommendations

Well-functioning labour markets are one of the keys to a country’s economic and social development. In Georgia, unemployment is a strong push factor for emigration. It is therefore important to identify the extent to which Georgia’s various labour market policies affect the migration decisions of households and individuals.

This chapter confirms that highly skilled occupational groups, especially the health sector are losing more labour to emigration than the other skills groups. Households respond to emigration and remittances by reducing their supply of labour to the market. Individuals who receive remittances are more likely to be unemployed, especially women. Return migrants tend to be self-employed after their return.

Government employment agencies are in place in Georgia, providing job seekers with better information on the domestic labour market, thereby increasing market efficiency. Vocational training programmes have become one of Georgia’s key labour market strategies to strengthen skills. However, the IPPMD survey found that the direct and indirect impact of these two labour market tools on migration decisions was limited.

While policies are needed to address the potential negative effects of migration and to amplify its positive effects on the labour market, labour market policies should also incorporate migration into their design. Here are some policy recommendations deriving from the findings in this chapter:

  • Widen the activities of employment agencies to reach out to both current emigrants abroad and return migrants at home to ensure they have information on and access to formal wage jobs. Closer connections between the employment agencies and the private sector will be important for achieving this.

  • Refine vocational training programmes to better target and match demand with supply. Mapping labour shortages and strengthening co-ordination mechanisms with the private sector are important steps. Training programmes can also aim to foster the inclusion of return migrants into the labour market.

References

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Adams, Jr. R.H. and J. Page (2005), “Do international migration and remittances reduce poverty in developing countries?”, World Development, Vol. 33/10, pp. 1645-1669.

Ammassari, S. (2004), “From nation-building to entrepreneurship: the impact of élite return migrants in Côte d’Ivoire and Ghana”, Population Space Place, 10: 133-154, doi: 10.1002/psp.319.

De Vreyer, P., F. Gubert, and A. S. Robilliard (2010), “Are there returns to migration experience? An empirical analysis using data on return migrants and non-migrants in West Africa”, Annals of Economics and Statistics, Vol. 97/98, pp. 307-28, http://www.jstor.org/stable/41219120.

Dustmann, C. and O. Kirchkamp (2002), “The optimal migration duration and activity choice after re-migration”, Journal of Development Economics, Vol. 67/2, pp. 351-372.

EPRC (2011), “The Role of Remittances in Georgian Economy”, Economic Policy Research Center, Tbilisi.

Government of Georgia (2014), Social-economic Development Strategy of Georgia – Georgia 2020, Government of Georgia, Tbilisi, http://static.mrdi.gov.ge/551e4a570cf24147438b1727.pdf.

Grigorian, D. A. and T. A. Melkonyan (2011), “Microeconomic Implications of Remittances in and Overlapping Generations Model with Altruism and a Motive to Receive Inheritance”, Journal of Development Studies, Taylor & Francis Journals, vol.48(8), pp. 1026-1044.

ILO (2015), Global Employment Trends for Youth 2015: Scaling up Investments in Decent Jobs for Youth, International Labour Organization, Geneva.

ILO (2012), International Standard Classification of Occupations: ISCO-08, International Labour Office, Geneva.

Kim, N. (2007), “The impact of remittances on labor supply: The case of Jamaica”, Policy Research Working Papers, No. 4120, World Bank, Washington, DC.

Mesnard, A. (2004), “Temporary migration and capital market imperfections”, Oxford Economic Papers, Vol. 56/2, pp. 242-262.

Ministry of Education and Science of Georgia (MoES) (2013), Vocational Education and Training Development Strategy for 2013-2020. http://www.mes.gov.ge/uploads/12.%20VET%20Strategy%202013-20_EN.pdf.

National Statistics Office of Georgia (GeoStat) (2014), Employment and Unemployment 2014 (Annual). http://www.geostat.ge/cms/site_images/_files/english/labour/employment%20and%20unemployment%202014%20press%20release.pdf .

OECD (2017), Interrelations between Public Policies, Migration and Development, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264265615-en.

Woodruff, C. and R. Zenteno (2007), “Migration networks and microenterprises in Mexico”, Journal of Development Economics, Vol. 82/2, pp. 509-528.

World Bank (2013), Georgia: Skills Mismatch and Unemployment, Labor Market Challenges, World Bank, Washington, DC, https://openknowledge.worldbank.org/handle/10986/15985.

Yang, D. (2008), “International migration, remittances and household investment: evidence from Philippine migrants’ exchange rate shocks”, The Economic Journal, Vol. 118/528, pp. 591-630.

Notes

← 1. People are considered to be employed not only when they are hired for a private or government sector job that generates income as a salary, but also when they perform work with a view to gaining profit, income (in cash or in kind), or other benefits. The self-employed are people who work in their own enterprise or household and have their own income.

← 2. Unemployment rate has been decreasing since 2009 according to GeoStat.

← 3. Discouraged workers constitute one group of inactive work-seekers. These are persons who, while willing and able to engage in a job, are not seeking work or have ceased to seek work because they believe there are no suitable available jobs. http://stats.oecd.org/glossary/detail.asp?ID=645

← 4. Though the difference is not statistically significant (using a chi-squared test).