Chapter 5. How do sectoral policies affect migration in Armenia

Although not specifically targeted at migration, sectoral policies in key areas for development – such as the labour market, agriculture, education, and financial services and investment – can also affect migration decisions. The IPPMD household and community surveys explored a wide set of policy programmes in these four sectors to identify the links between sectoral policies and migration. This chapter reports on analysis of the ways in which policy programmes in these sectors in Armenia influence people’s decisions to emigrate, to send remittances and to return home.

  

Migration is inevitably influenced by policies in the country of origin. Most countries have a set of policies which directly target migration, such as those controlling who can enter the territory and under which conditions, and those aiming to facilitate the sending and receiving of remittances. However, policies not specifically targeted at migration can also have an influence on migration dimensions in the sectors that are key to development and explored in Chapter 4: the labour market, agriculture, education, and investment and financial services.

Chapter 4 showed that the migration characteristics of these four sectors vary. The policy context for each of these sectors in turn influences migration outcomes, such as the decision to emigrate, to return and how remittances are used. To date, the impact of sectoral policies on migration remains largely unresearched. This chapter attempts to disentangle the links between sectoral policies and migration in Armenia by examining a wide set of policy programmes in the four sectors (Table 5.1).

Table 5.1. Sectoral policies and programmes covered in the IPPMD project

Sectors

Policies / programme

Labour market

  • Government employment agencies

  • Vocational training programmes

  • Public employment programmes

Agriculture

  • Subsidy-type programmes

  • Agricultural training programmes

  • Insurance-based programmes

Education

  • In-kind distribution programmes

  • Cash-based programmes

  • Other types of education programmes

Investment and financial services

  • Policies related to businesses investments

  • Policies related to financial inclusion and education

This chapter is organised according to the four sectors under study. It first discusses how migration outcomes are affected by labour market policies, followed by policies governing agriculture, education, and investment and financial services.

Labour market policies and migration

IPPMD data confirm that the search for jobs is the main driver of emigration from Armenia. About 80% of current emigrants reported that they left the country to take or search for jobs abroad (Chapter 3). Policy instruments that improve the domestic labour market may therefore reduce the incentive to migrate.

The IPPMD study focuses on policies that aim to enhance labour market efficiency through government employment agencies, improve the skills set of labour through vocational training programmes, and expand labour demand by increasing public employment programmes. It asks to what extent are these policies present in Armenia, and are they having an influence on migration?

Vocational training programmes tend to curb emigration in Armenia

The Armenian government is increasing its attention to vocational education and training (VET). The National Center for VET Development under the Ministry of Education and Science is responsible for increasing the efficiency of preliminary (handicraft) and vocational education and training. This includes adult education system reforms to foster its development, international integration, and the international recognition of certificates and qualifications awarded in the Republic of Armenia. It is involved in developing VET strategy and policy, and medium and long-term development programmes and action plans for the development of the VET system; organising and implementing analyses of VET system; and participating in the rationalisation of the VET system, including developing proposals concerning the creation, reorganisation, liquidation, allocation and revision of activities.

The IPPMD survey found that 9% of the labour force surveyed had participated in a vocational training programme in the five years prior to the survey. A higher share of women took part in vocational training than men: 13% versus 6%. There was no discernible difference between participation rates in rural and urban areas. Survey findings indicate that the type of training programmes women and men take differ. While the most common programmes for women are education or health-related (27% and 17%, respectively), men were more likely to seek training in computing/information technology (13%), followed by electricity/plumbing (8%) and mechanics (7%).

Vocational training programmes can affect migration in two different ways. While they might help people secure better jobs in the domestic labour market, thereby reducing the need to migrate, they might also make would-be migrants more employable overseas. A comparative study of the ten IPPMD partner countries shows that in most countries the share of people planning to migrate is higher among those who had participated in a vocational training programme than among those who did not (OECD, 2017). This suggests that in general, people participate in vocational training programmes in order to find a job abroad. Armenia, however, is an exception to this pattern. A lower share of people who took part in trainings plan to emigrate compared to non-participants: 7% versus 12%. The difference is statistically significant.

This pattern is explored in a regression analysis (Box 5.1).1 It examines the links between participating in vocational training programmes and plans to emigrate while controlling for other factors, such as unemployment. The results (shown in Table 5.2) indicate a negative link between vocational training programmes and plans to emigrate, particularly for men. As seen in Chapter 4, in Armenia the propensity to emigrate is higher among the lower-skilled occupational groups than higher-skilled groups. Thus, vocational training programmes could be promoting upward labour mobility and reducing incentives to look for jobs abroad. The results also suggest that being unemployed appears to push people to emigrate. Having an emigrant member in the household also raises the propensity to move abroad.

Box 5.1. Participation in a vocational training programme reduces men’s plan to emigrate

To investigate the link between participation in vocational training programmes and having plans to emigrate, the following probit model was used:

Prob( picture (1)

where picture represents whether individual i has a plan to emigrate in the future. It is a binary variable and takes a value of 1 if the person is planning to leave the country; picture is the variable of interest and represents a binary variable indicating if the individual participated in a vocational training programmes in the five years prior to the survey; picture stand for a set of control variables at the individual level and picture for household level controls;a picture implies regional fixed effects and picture is the randomly distributed error term. The model has been tested for two different groups: men and women. The coefficients of the variables of interest are shown in Table 5.2.

Table 5.2. Participation in a vocational training programme reduces men’s plan to emigrate

Dependent variable: Individual plans to emigrate

Main variables of interest: Individual has participated in a vocational training programme

Type of model: Probit

Sample: Labour force in working age (15-64)

Variables of interest

Sample

All

Men

Women

Individual participated in a vocational training programme

-0.039

(0.024)

-0.096**

(0.041)

-0.012

(0.025)

Household has at least one emigrant

0.078***

(0.016)

0.106***

(0.025)

0.056***

(0.019)

Individual is unemployed

0.068***

(0.014)

0.098***

(0.020)

0.031*

(0.017)

Number of observations

2 856

1 632

1 224

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

a. Control variables include age, sex, education level of individuals and whether the individual is unemployed or not. At the household level, the household’s size and its squared value, the dependency ratio, its wealth indicator and its squared value are controlled for. Whether the household has an emigrant or not is also controlled for.

Government employment agencies and public employment programmes are doing little to influence migration

The Ministry of Labor and Social Affairs (MLSA) is responsible for the design and regulation of labour market policies. The law regulating this field is the Law on Employment (adopted 11 December 2013), which provides the legal framework for the promotion of employment and the regulation of social protection of unemployed people. Each year the Armenian Government, through its Protocol Decree, approves the list of labour market policies to be implemented.

The State Employment Agency (SEA) is an agency within the MLSA to which regulatory functions in the sphere of employment are delegated. The SEA covers the entire country through its extensive network of 51 regional or territorial employment centres (10 marz and 41 regional centres) and the central office in Yerevan. Its main functions are:

  • implementing active labour market programmes (in 2014 Armenia abandoned unemployment benefit provision and focuses only on activation policies)

  • the regular collection and analysis of data on the labour market.

Government employment agencies can have an indirect impact on households’ migration decisions. If people can find jobs in the local labour market through such agencies, they may choose to stay rather than emigrate to seek work. However, in the IPPMD sample only about 2% of Armenians employed in either the public or private sector had found jobs through government employment agencies (6% for men and 2% for women). Most people had found their job through friends and family or by approaching potential employers directly (Figure 5.1). Together these two methods account for 87% of all surveyed adults with paid jobs in both the public and private sector.

Figure 5.1. Government agencies play a minor role in job seeking among Armenian IPPMD respondents
Methods for finding a current job in both public and private sectors
picture

Source: Authors’ own work based on IPPMD data.

While the share of people who benefited from government employment agencies is low, there are certain patterns related to migration. Of those who found their jobs through a government employment agency, only 3% have plans to emigrate, while a much bigger share of those who did not use these agencies plan to emigrate (10%). Individual characteristics of government employment agency beneficiaries explain this pattern. Beneficiaries are in general more highly educated than non-beneficiaries and are more likely to hold jobs in the public sector, which are seen as secure occupations.

Public employment programmes (PEPs) in Armenia support the renovation of social infrastructure (e.g. schools, kindergartens, medical institutions, social security institutions and cultural institutions), and the improvement of roads, parks, playgrounds, historical monuments, museums, churches and the like. The programme is implemented across RA marzes, with mountainous and border areas having high priority. Unemployed job seekers may participate in this programme more than once. The maximum duration of the programme is three months. Each participant is paid AMD 5 000 (USD 12) per day, including income tax and targeted social fees.

PEPs can either increase or decrease the incentives to migrate. Programmes which improve local employment opportunities may encourage people to stay. In rural areas in particular, public works programmes for agricultural workers during the farming off-season can provide an alternative to seasonal migration. On the other hand, the increased income received from cash-for-work programmes can help people afford to migrate. Overall, the impact of PEPs on migration is likely to depend on their duration, coverage and income level. Results of the IPPMD household survey in Armenia indicate low participation in these programmes among employed and unemployed people (less than 1%). This small sample size prevented further analysis from being done.

Agricultural policies and migration

Migration decisions are also influenced by policies in the agricultural sector. This section investigates these links for Armenia, where agriculture is a sector of high importance. In fact, it is one of five priority sectors for which direct policies are discussed in Armenia’s Strategic Program of Prospective Development for 2014-2025 (SPPD), due to it being a key link in the value-chain for the food industry, and its large export potential (RA, 2014). The strategic programme also underlines development and growth in productivity of the agricultural sector as main factors in the creation of non-agricultural jobs in rural areas, vital for the country’s economic diversification. Despite its vital role in Armenia’s economy, however, insufficient investment has meant that productivity is limited and many smallholder farmers live in poverty, unable to realise the potential of their land (Oxfam, 2016; FAO, n.d.). Nearly half of Armenia’s 200 000 farms operate on a subsistence basis (Mnatsakanyan et al., 2015). Policy concerns in the sector are therefore primarily aimed at food sufficiency and living standards (FAO, n.d.).

Partly as a response to the lack of investment, and to support the Armenian government, the European Union has earmarked the agricultural sector as being of strategic importance for the development of the country. It is providing EUR 25 million over three years starting in 2014, within its larger European Neighbourhood Programme for Agriculture and Rural Development (ENPARD). The programme is particularly aimed at subsistence farming, improving institutions, strengthening capacity and increasing access to affordable food.

A major concern of the government has been access to financial means for farmers, and has historically set measures to promote their access to credit. A key programme was initiated by the government in 2010-11, subsidising the interest rates on financial credit, for instance (RA, 2014).2 The government has enlarged the programme since 2011, making loans increasingly available over time (Mnatsakanyan et al., 2015).

In fact, agricultural subsidies are of particular importance in Armenia. The SPPD lists tariff and subsidy programmes as a key policy direction for the future of the sector (RA, 2014). In 2012, the Ministry of Agriculture launched a seed subsidy programme, through which farmers obtain a kilogramme of seeds in exchange for 2 kilogrammes of grain after harvest. The government also provides subsidies for other inputs, such as nitrogen fertilisers and diesel fuel (Mnatsakanyan et al., 2015). In addition, the government ran a programme in Armenia’s 915 communities offering veterinary and sanitary services to farmers, including research on more weather-efficient crops.

Access to water and proper irrigation is also an issue for farmers in Armenia. As the cost of accessing irrigation water is high in Armenia, the government subsidises a share of farmers’ irrigation expenses, and specifically when the costs surpasses USD 0.026 per cubic metre of water (Mnatsakanyan et al, 2015).3

The government has also spearheaded other policies. Agricultural insurance programmes have, for the most part, been limited to financial and in-kind compensation to farmers affected by weather and climate-related disasters. However, as climate change has been identified as a strategic concern for the sector, the government plans to launch an agricultural insurance programme in 2017, aimed at both agrarian farming and cattle breeding (Agroinsurance, 2016; Tatin-Jaleran, 2014). The insurance programme will help farmers cope with expected changes in rainfall and temperature. In addition to subsidies and insurance programmes, the government also provides training and consultancy services to farmers, enabling them to adopt new farming techniques and increase production. However, these programmes are rather limited in coverage.

The IPPMD project collected data on several types of these policies, including agricultural subsidies, training programmes and insurance-related programmes, such as crop insurance, contract farming and cash-for-work programmes. Households were asked to list each year in which they benefited from each programme between 2010 and 2014. Overall, 236 of the 1 001 (24%) agricultural households surveyed benefited from at least one agricultural programme from 2010 to 2014, with the vast majority benefitting from agricultural subsidies (23% of agricultural households). These covered a large range of agricultural subsidies, including for seeds (96 households, 10%), veterinarian services (83 households, 8%) and fuel (71 households, 7%). However, the most common agricultural subsidy was for inputs other inputs (i.e. other than for seeds or fuel (136 households, or 14%).

Apart from agricultural subsidies, only five households benefited from agricultural training,4 and 31 households benefited from an insurance-related programme (3%). Amongst these households, most claimed to have benefited from compensation following a weather shock to their crops (29 households, 3%).

Because of their pertinence in Armenia, the analysis focuses on agricultural subsidies. It is not always clear whether agricultural subsidies have a net positive or negative effect on migration and remittance flows. By increasing the household’s income, they reduce financial constraints. In doing so, they may reduce the household’s need to seek income elsewhere, and thus reduce emigration pressure. On the other hand, they may provide enough additional income to cover the costs of emigration. Or they may provide the incentive for households to invest and channel funds towards agricultural activities, thus increasing the need for remittances, or they may make them less necessary, thereby reducing their flow. What does the IPPMD data analysis tell us about these effects of subsidies on migration?

Agricultural subsidies tend to decrease plans to emigrate

The descriptive statistics show that agricultural subsidies in general have only a slight influence on reducing emigration. Households benefiting from agricultural subsidies were less likely to have a member planning to emigrate (14% vs. 17%) and less likely to have had an emigrant in the past 5 years (18% vs. 22%) compared to households that did not benefit from agricultural subsidies (Figure 5.2). Both of these differences were not statistically significant. In addition, agricultural subsidies tend to be positively correlated with migrant households that have a return migrant member. Overall, 58% of migrant households benefiting from subsidies have a return migrant, compared to 47% for non-subsidised migrant households. This lends support to the notion that agricultural subsidies help households attenuate the financial issues that may have driven a member to leave, encouraging them to return home. Thus, agricultural subsidies appear to create incentives to return.

Figure 5.2. Agricultural subsidies are linked to lower emigration and increased return migration
Share of households by migration dimension and whether it benefited from an agricultural subsidy (%)
picture

Note: Statistical significance calculated using a chi-squared test is indicated as follows: ***: 99%, **: 95%, *: 90%.

Source: Authors’ own work based on IPPMD data.

Regression analysis probed the links between agricultural subsidies and migration outcomes further (Box 5.2). The results confirm that agricultural subsidies in general tend to decrease the probability that a household has a member that plans to emigrate (Table 5.3, row 1). However, they are not significantly statistically linked to any other outcome, despite the descriptive statistics in Figure 5.2 suggesting a positive influence on return migration. The regression results show that the household’s administrative region is an important determinant of the migration outcomes, and likely of whether the policies were accessible as well. This is the main reason why a positive result is not found for return migration. In regression results where the administrative region is not controlled for, agricultural subsidies are positively linked to return migration.5

Box 5.2. The links between agricultural subsidies and migration

To estimate the probability that an agricultural subsidy (or its absence) affected a migration-related outcome, the following probit regression model was estimated:

picture (2)

where the unit of observation is the household hh and the dependent binary variable migration_outcomehh takes on a value of 1 if the household has a migration event take place and 0 otherwise. picture represents a dummy variable taking the value of 1 if the household benefited from an agricultural subsidy. picture stands for a set of household-level regressors while picture represents regional-level fixed effects.a Standard errors, picture, are robust to heteroskedasticity.

Results for four outcomes are presented in Table 5.3. Column (1) shows results reflecting the probability that the household had a member planning to emigrate; column (2) a binary variable equal to 1 if the household has had at least one member emigrate in the past 5 years (excluding households that had a member emigrate prior to that); column (3) a binary variable equal to 1 if the household has received remittances from any source in the past 12 months; and column (4) a binary variable equal to 1 if the household has had a member return from emigration within the past 5 years (including households with either returned or current emigrants).

Table 5.3. Agricultural subsidies decrease plans to emigrate

Dependent variable: Migration outcomes

Main variables of interest: Household benefited from an agricultural subsidy

Type of model: Probit

Sample: Agricultural households

Variables of interest

Dependent variables

(1)

Household has a member planning to emigrate

(2)

Household has a member leave within 5 years

(3)

Household received remittances in the past 12 months

(4)

Household has had a member return in the past 5 years (amongst migrant households)

Benefited generally from an agricultural subsidy in the past 5 years

-0.052*

(0.027)

-0.009

(0.036)

-0.000

(0.041)

0.007

(0.067)

specifically for seeds

-0.041

(0.049)

0.015

(0.053)

0.044

(0.059)

-0.020

(0.092)

specifically for fuel

-0.057

(0.041)

0.017

(0.056)

-0.059

(0.057)

-0.060

(0.102)

specifically for other inputs

-0.042

(0.031)

0.006

(0.041)

0.003

(0.047)

0.067

(0.075)

specifically for veterinary services

0.021

(0.050)

0.014

(0.055)

0.035

(0.061)

-0.142*

(0.083)

Number of observations

1 001

876

1 001

508

Note: Statistical significance is indicated as follows: ***: 99%, **: 95%, *: 90%. Coefficients reflect marginal effects. Standard errors are in parentheses and robust to heteroskedasticity.

a. Control variables for the model include the household’s size, its dependency ratio (number of children aged 0-15 and elderly aged 65+, divided by the total of other members), the male-to-female adult ratio, its wealth estimated by an indicator (see Chapter 3), whether it is in a rural or urban region and a fixed effect for its administrative region.

How are these migration outcomes related to each individual subsidy programme in Armenia? The same regressions were individually run again for each of the top four subsidy programmes (subsidies for seeds, fuel, other inputs and veterinarian services) to investigate whether the findings are related to one specific programme. Breaking down the findings in this way suggests that no specific subsidy programme is driving the results on the negative link shown earlier between agricultural subsidies and plans to emigrate. The results also confirm that agricultural subsidies have no influence on actual emigration. In terms of remittances, no specific programme seems to be substituting for remittances. However, because no link is found between emigration and agricultural subsidies, it is not surprising that remittances, which are sent by emigrants, are also not linked with such programmes. Running regression on remittances, while controlling for the fact that a household has an emigrant, reveals that, agricultural subsidies for fuel, specifically, are negatively linked with remittances (not shown). This lends support for the fact that they are substituting remittances. Households that receive financial aid for fuel from the state are in less need of remittances.

On the other hand, subsidies for veterinary services are negatively linked to return migration (Table 5.3). This is unexpected because it suggests that receiving subsidies for such services does not incentivise emigrants to return and rather it is correlated with them staying abroad. A plausible reason for this is that the regions in the highest proportion of household benefit from such services (the provinces of Shirak and Vayots Dzor) are poor, with few opportunities for return migrants to come back to. The low level of jobs available in these regions also means that the need for households to receive remittances from emigrants remains high. The free veterinary services offered by the state are not enough to make a big enough difference to these households’ livelihoods.

The administrative fixed effect in the regression model suggests that households’ regions are a major determinant of the link between the subsidies and migration outcomes. In fact, rerunning the regressions without the fixed effect suggests that the negative link between agricultural subsidies and plans to emigrate is primarily driven by agricultural subsidies for fuel, suggesting that fuel costs may be an important determinant of emigration. Reducing those costs may be key to reducing emigration. The results also suggest that subsidies for inputs other than seeds and fuel drive the positive link between agricultural subsidies and return migration (not shown).

Education policies and migration

The Government of Armenia has implemented multiple programmes to improve and strengthen the education sector over the past two decades. The Center for Education Projects (PIU), under the Ministry of Education, was established in 1996 to carry out projects aimed at improving the quality, relevance, access and effectiveness of the Armenian education sector. Since its establishment, three main reform projects have been implemented in order to create an educational system that effectively can meet the demands and expectations of the country’s social and economic development, in line with the Government’s economic development strategy:

  1. The Education Financing and Management Project (1998-2002) aimed to ensure financing of the general education system and increase efficiency in the use of resources in the education sector.

  2. The Education Quality and Relevance reform was carried out in two phases: Phase I in 2003-2009 and Phase II 2009-2014. Its overall aim was to direct and adjust the development of the education sector to the demands of a knowledge-based economy.

  3. The Education Improvement project (2014-2019) is currently in place and focuses on the improvement of education quality at all levels (PIU, n.d.).

Other public and international institutions have also implemented specific projects related to migration and education. At least 19 projects were implemented by both local and international institutions in 2000-2014 related to migrant support from a skills and employment perspective. The programmes addressed different phases of the migration cycle, although most of them focused on the post-migration phase, targeting the return and reintegration of returning migrants (ETF, 2015). The IPPMD stakeholder interviews (see Chapter 3) also revealed that specific programmes to support return migrants have been implemented by international organisations, local NGOs and government. Examples include requalification courses and training to improve skills to meet the demands of the local labour market. In addition, local NGOs and civil society organisations (CSOs) have worked on developing skills of returnees, as well as provided short-term grants for students wishing to study abroad.

Apart from education programmes especially targeting migrants and return migrants, general education policies and programmes can affect migration patterns in various ways. Young individuals or parents may decide to emigrate if educational conditions are not up to standard for themselves or for their children. Government investments in education through education policies and programmes may decrease the incentives to emigrate if the motivation for emigration was to finance the education of children in the household or to seek better quality education. On the other hand, educational cash-based programmes may also give households the means to emigrate. This section investigates these types of education policies and programmes, and also discusses the link between migration and skills mismatches.

Education programmes do not seem to influence migration patterns

The IPPMD household surveys conducted in the ten partner countries included questions on a variety of education programmes. These can roughly be divided into three categories: cash-based, in-kind and other types of programmes. Of these, in-kind distribution programmes are the most common according to the Armenia survey.6 Households with children mostly benefit from two such programmes: the provision of textbooks (36% of households with an emigrant and 38% of households without an emigrant) and school meal programmes (23% of households with an emigrant and 24% of households without an emigrant). The other educational programmes had a beneficiary rate of less than 5% in the Armenian survey (Figure 5.3).7 This shows that households with emigrants are slightly less likely to benefit from education programmes. Overall, 40% of households with emigrants and 42% of households without emigrants benefited from an education programme.

Figure 5.3. Households with and without emigrants are equally likely to benefit from educational programmes
Share of households with children that have benefited from educational programmes in the past five years
picture

Note: Sample only includes households with children in school age (6-20 years old). Only programmes benefitting more than 1% of households in the sample are displayed.

Source: Authors’ own work based on IPPMD data.

The comparative report of the IPPMD project showed that cash-based programmes tend to have the greatest influence on migration decisions, particularly remittances (OECD, 2017). The only Armenian cash-based programme included in the household survey was scholarships for tertiary education at Armenian universities (interstate programmes also provide a very limited number of scholarships to students who want to study at international universities). The scholarship programme is the only programme where households with an emigrant are more likely to benefit than households without an emigrant in the Armenian sample (Figure 5.3), although the difference is marginal (2.2% vs. 1.9%). The sample size of households in the IPPMD data benefiting from scholarships is too small (24 households) to allow separate analysis of these programmes.

The relationship between education policy programmes and migration is further analysed in Box 5.3, using regression analysis. The results show no significant link between beneficiary households and having an emigrant, having a member planning to emigrate or receiving remittances. Although the relationships are all positive, no statistically significant associations were found when controlling for household characteristics. One potential explanation is the nature of the policy programmes identified in the Armenian survey, which are mainly in-kind distribution programmes. Cash-based programmes are potentially more likely to influence household migration decisions and behaviour (OECD, 2017), however there are few such programmes in Armenia.

Box 5.3. The links between education policies and migration

To estimate the association between a household benefiting from any education programme and migration outcomes (plans to emigrate, having an emigrate, receiving remittances), the following probit equation is applied:

picture (3)

where picture represents household migration status, being a binary variable for the household either having at least one member planning to emigrate in the future (specification 1) having at least one emigrant who left in the five years prior to the survey (specification 2) or household receiving remittances (specification 3). picture is the variable of interest and represents a binary variable indicating if the household has benefited from an education policy in the five years prior to the study. It takes on value “1” if the household has benefited from an education policy programme and “0” otherwise. picture are set of observed individual and household characteristics influencing the outcome.a picture represents regional-level fixed effects. Standard errors, εhh, are robust to heteroskedasticity.

Table 5.4. Education policies do not affect migration patterns

Dependent variable: Household with emigrant/member planning to emigrate/remittances

Main variables of interest: Household benefited from education programme

Type of model: Probit

Sample: All households

Variables of interest

Dependent variable

(1)

Plan to emigrate

(2)

Household has an emigrant

(3)

Household receives remittances

Household benefited from any education policy in the past 5 years

0.033

(0.029)

0.010

(0.028)

0.014

(0.023)

Number of observations

931

1 662

1 841

Note: Statistical significance is indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors are in parentheses and robust to heteroskedasticity. The emigrant household sample is restricted to emigrant households with a member who emigrated abroad in the five years prior to the survey in order to capture the timing of the migration decision and the policy intervention. Households with emigrants who left more than five years previously are excluded. Analysis was also performed on a sub-sample of households with children of school age (6-20 years), but this did not change the results.

a. The control variables include household size, household dependency ratio (defined as the number of children and elderly in the household as a share of members in working age), the mean education level of adults in the household, the number of young children (aged 6-14) and the number of youth (aged 15-17) in the household, a dummy for urban location, an asset index aiming to capture household wealth, and regional fixed-effects.

Addressing skills mismatches could enhance migration’s development potential

On the one hand Armenia has been thought to be disadvantaged by the fact that many of its highly educated citizens are emigrating (Makaryan and Galstyan, 2012). On the other hand, emigration has the potential to contribute to sustainable development through the knowledge and experience brought home by returning migrants (UNDP, 2009; Gevorkyan, Gevorkyan and Mashuryan, 2006). However, as shown in Chapter 4, few of the migrants who return to Armenia have obtained education abroad. One reason may be the barriers that particularly skilled return migrants face in joining the labour market on their return. Armenians who return from abroad, particularly the young, are struggling to find jobs that match their knowledge and skills (Manasyan and Poghosyan, 2012).

The Armenian professional educational system face challenges in adjusting to labour market needs. Many Armenians acquire skills that cannot be properly used at home or abroad (Makaryan and Galstyan, 2012; ILO, 2009). These mismatches between the labour market needs and the knowledge and skills of the workforce are also prevalent among return migrants.

These challenges are also reflected in the IPPMD data on return migrants. More than half of the return migrants in the IPPMD sample (54%), found it hard to find a job on their return. In addition, 4% stated that it was hard to find a job that corresponded to their education level. This share was higher among return migrants with post-secondary education (6%) than those with lower levels of education (3%). Other studies found that 28% of returnees reported having jobs below their education level (Collyer et al., 2013). In order to turn emigration and return migration into an opportunity, policy measures are needed to better align the education curricula with the local labour market in Armenia. Policies are also needed to make sure that knowledge and skills brought back by return migrants are recognised and used optimally. This will help to attract more highly skilled migrants back and improve how their skills are used for development.

Investment and financial services policies and migration

Financial inclusion has been broadly recognised as critical for reducing poverty and achieving inclusive economic growth. The use of formal bank accounts and savings and payment mechanisms increases savings, empowers women, and boosts productive investment and consumption (Demirguc-Kunt et al., 2015). As reported in Chapter 4, the small but growing Armenian financial system is dominated by its banking sector. The share of individuals with a bank account and savings in a financial institution is however quite low among the Armenian population. In 2014 only 18% of individuals aged 15 and above had an account in a financial institution, compared with 40% of adults in neighbouring Georgia (OECD, 2017). Moreover, among account holders less than 10% actually use their account for savings (Demirguc-Kunt et al., 2015). Overall, only 2% of the adult population is saving money in a financial institution, while 20% indicated having borrowed from a financial institutions and 27% borrowed money from their family or friends in 2014 (World Bank financial inclusion database, n.d.). The low savings rate is partly due to the underdeveloped financial sector, but also to a declining trust in the banking system. Opinion survey data show that only 34% of surveyed individuals claimed that they had trust in the banking system in 2015, down from 53% in 2008 (The Caucasus Barometer, n.d.).

Financial services provision and access are limited

The IPPMD community survey (see Chapter 3) collected information on financial institution coverage in the surveyed communities. The data show a clear difference between rural and urban areas. All three types of financial institution – microcredit organisations, money transfer operators and banks – are much more common in urban areas than in rural areas. Almost all urban areas have a bank (96%) and a microcredit organisation (86%), while only 2% of the rural communities have a bank office or 6% a microcredit organisation (Figure 5.4). The IPPMD household data show that 37% of households in the sample have a bank account. The share is higher in urban areas, where 40% of household are account holders, compared to 33% in rural areas.

Figure 5.4. Urban communities are significantly better served by financial service institutions
Share of communities with financial institutions (%), by geographic region
picture

Source: Authors’ own work based on IPPMD data

Having access to the formal financial sector can strengthen the development impact of remittances by encouraging more savings and better matching of savings with investment opportunities (UNDP, 2011). Channelling remittances through formal financial institutions is often more secure and can also contribute to the development of the financial system and make resources available to finance large-scale economic activities beyond the investments made by the recipient households. Previous research shows that remittance-receiving households in Armenia tend to save more, but are not more likely to take out a formal bank loan (Grigorian and Melkonyan, 2012).

The IPPMD descriptive statistics show that remittance-receiving households with a bank account received on average higher amounts of remittances in the past 12 months (USD 2 333) than remittance-receiving households without a bank account (USD 1 978). However, households with and without bank accounts are as likely to receive remittances through informal channels: on average 5% for both groups (Figure 5.5).

Figure 5.5. Households with a bank account receive on average more remittances
Yearly average amount of remittances received (USD), and share of households receiving remittances through informal channel, by bank account status
picture

Source: Authors’ own work based on IPPMD data.

The relationship between having a bank account and remittance patterns is further investigated in Box 5.4. This regression analysis tells a different story: the association between having a bank account and the amount of remittances a household receives is negative (although the association is not statistically significant). It seems that other household characteristics play a role. One driving variable is household wealth, which to be positively associated with a household having a bank account, as well as the amount of remittances the household receives. This partly explains the positive association between having a bank account and amounts of remittances depicted in Figure 5.5. Performing the analysis in rural and urban areas (results not shown) shows that the association is positive in urban areas but negative in rural areas (although still not statistically significant), indicating that different dynamics are at play. This may in turn be linked to the large difference in financial service coverage in rural and urban areas. In addition, the regression analysis found no link between having a bank account and receiving remittances through informal channels.

Box 5.4. The links between bank accounts and remittance-sending behaviour

Regression analyses were applied to estimate the link between bank accounts on remittance patterns, using the following two models:

picture (4)

picture (5)

where the dependent variable in model (1) represents the probability of receiving informal remittances, and in model (2) the amount of remittances the household receives. picture represents a binary variable indicating if the household haves a bank account, where “1” denotes a household with a bank account and “0” if not. picture are a set of observed household and individual characteristics influencing the outcome.a picture represents regional-level fixed effects. Standard errors, εhh, are robust to heteroskedasticity.

Table 5.5. Access to a bank account does not seem to influence remittance patterns

Dependent variable: Amount of remittances received/household receives formal remittances

Main variables of interest: Household has a bank account

Type of model: Probit/OLS

Sample: All households receiving remittances

Variables of interest

Dependent variables

(1)

Amount of remittances received

(2)

Household receives informal remittances

Household has a bank account

-328.3

(407.7)

-0.004

(0.031)

Number of observations

262

478

Note: Statistical significance is indicated as follows: ***: 99%, **: 95%, *: 90%. Standard errors are in parenthesis and robust to heteroskedasticity.

a. The control variables include household size, household dependency ratio (defined as the number of children and elderly in the household as a share of members in working age), the mean education level of adults in the household, the number of young children (aged 6-14) and the number of youth (aged 15-17) in the household, a binary variable for female head and for urban location, an asset index aiming to capture household wealth, and regional fixed-effects.

Very few households have participated in financial training

Financial training programmes and business management courses help to build financial literacy, which can encourage investment in productive assets. In order to enable households to maximise the returns to their remittance investments, they need to have information about the investment products available, as well as saving and investment opportunities. Knowledge about business management is also important for households that might want to invest in setting up a business. This applies to both households receiving remittances and households in communities where remittances inflows are high and trickling down to the local economy.

The IPPMD household survey asked households whether they had participated in a financial training programme in the previous five years. This revealed that fewer than 1% of households in Armenia have benefited from a financial training programme (Figure 5.6). This can be compared to the overall participation rate in the IPPMD household survey sample for all ten countries, which is about 5% (OECD, 2017). On the other hand, more than 30% of the communities in the Armenian IPPMD sample offer courses in banking, financial tools and entrepreneurship (Figure 5.6), which is better than in most other IPPMD countries (OECD, 2017). However, these courses are much more widespread in urban than in rural areas, so there is scope to increase the coverage of financial training programmes in rural areas, as well as to increase household participation in these courses in both urban and rural areas, in order to encourage and enable more long-term remittance investments.

Figure 5.6. Household participation in financial training programmes is very low
Share of communities which offer financial training (left graph); share of households participating in financial training programmes (right graph)
picture

Source: Authors’ own work based on IPPMD data

Conclusions

This chapter has identified some clear links between sectoral policies and migration in Armenia. For instance, vocational training programmes appear to curb emigration, perhaps because they promote upward labour mobility in the local labour market and reduce the incentives to seek jobs abroad. Agricultural subsidies appear to provide current emigrants with incentives to return, possibly because they have removed the financial constraints which drove them to leave.

Other labour market programmes, such as government employment agencies and public employment programmes, are found to have little influence on migration, most probably due to their low coverage. Likewise, education policies do not seem to have any significant influence on households’ migration decisions. This result is likely partly explained by the nature of the policy programmes identified in the Armenian survey, which were mainly in-kind distribution programmes rather than cash-based programmes. For education polices to affect emigration decisions they would need to be more significant in their effect on correcting skills mismatches, as well as more widespread.

Participation in financial training programmes is very low among both migrant and non-migrant households in Armenia. There is scope to expand households’ access to bank accounts and financial training programmes to encourage the sending of remittances through formal channels and to enable households to invest them productively.

References

Agroinsurance (2016), “Armenia to introduce agriculture insurance system,” http://agroinsurance.com/en/armenia-to-introduce-agriculture-insurance-system/.

Caucasus Barometer (n.d.), Caucasus Barometer Armenian Dataset (database), Caucasus Research Resource Center, http://caucasusbarometer.org/en/datasets/ (accessed 15 March 2017).

Collyer, M., U. Bardak, E. Jansova and O. Kärkkäinen (2013), “Migration and skills in Armenia, Georgia and Morocco: comparing the survey results”, Working Paper, European Training Foundation Turin, www.etf.europa.eu/web.nsf/pages/Migration_and_skills_Armenia_Georgia_Morocco.

Demirguc-Kunt, A, L. Klapper, D. Singer and P. Van Oudheusden (2015), “The Global Findex Database 2014: Measuring financial inclusion around the world”, Policy Research Working Paper, No. 7255, World Bank, Washington, DC, http://documents.worldbank.org/curated/en/187761468179367706/The-Global-Findex-Database-2014-measuring-financial-inclusion-around-the-world.

ETF (2015), “Migrant support measures from an employment and skills perspective: Armenia”, European Training Foundation, Turin, www.etf.europa.eu/webatt.nsf/0/99FCA9FF7CAE628EC1257F3700614841/$file/MISMES%20Armenia.pdf.

FAO (n.d.), “Armenia at a Glance,” Food and Agriculture Organization of the United Nations, Rome, www.fao.org/armenia/fao-in-armenia/armenia-at-a-glance/en.

Gevorkyan, A., Gevorkyan, A. and K. Mashuryan (2006), Managed Temporary Labour Migration: Case of Armenia and Russia, Institute for the Economy in Transition, Moscow, www.iet.ru/files/text/guest/gevorkyan/gevorkyan.pdf.

Grigorian, A. D. and T.A. Melkonyan (2011), “Destined to receive: the impact of remittances on household decisions in Armenia”, Review of Development Economics 15(1): 139-53, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1745787.

Makaryan, G. and Galstyan, M. (2013), “Costs and benefits of labour mobility between the EU and the Eastern Partnership Partner Countries: Armenia Country report”, CASE-Center for Social and Economic Research No. 461/2013, Warsaw.

Manasyan, H. and Poghosyan, G. (2012),”Social Impact of Emigration and Rural-Urban Migration in Central and Eastern Europe: Final country report”, Armenia.

Mnatsakanyan, H., V. Urutyan and A. Yeritsyan (2015), “Country Report: Armenia,” International Center for Agribusiness Research and Education (ICARE), March 2015, Yerevan, www.agricistrade.eu/wp-content/uploads/2015/05/Agricistrade_Armenia.pdf.

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

Oxfam (2016), “Strengthening Armenia’s agricultural sector through multi-stakeholder networking: a case study on the agricultural alliance,” Oxfam Case Study, Oxford, http://oxfamilibrary.openrepository.com/oxfam/bitstream/10546/620114/1/cs-armenia-agriculture-networking-aa-241016-en.pdf.

PIU (n.d.), “Center for Education Projects” webpage, www.cfep.am/en (accessed 10 March 2017).

RA (2014), “Armenia Development Strategy for 2014-2025,” Annex to RA Government Decree #442 – N on March 27th, 2014, Republic of Armenia, Yerevan, https://eeas.europa.eu/sites/eeas/files/armenia_development_strategy_for_2014-2025.pdf.

Tatin-Jaleran (2014), “A needs assessment for introducing agricultural insurance in Armenia in the context of climate risk mitigation,” UNDP Armenia, Yerevan, https://info.undp.org/docs/pdc/Documents/ARM/Agriculture%20Insurance%20Report.pdf.

UNDP (2011), Towards Human Resilience: Sustaining MDG Progress in an Age of Economic Uncertainty, United Nations Development Programme, New York.

UNDP (2009), Armenian National Human Development Report 2009, Migration and Human Development: Opportunities and Challenges, UNDP Armenia, Yerevan.

World Bank (n.d.), Global Financial Inclusion Database, http://databank.worldbank.org/data/reports.aspx?source=global-findex (accessed 15 November, 2016).

Notes

← 1. See Chapter 3 for methodological background on the regression analyses used in this project.

← 2. Specifically, loans are provided to farmers at low annual interest rates of 14%, with the government covering 4% to 6% of these rates, with maturity coming at a maximum of two years and a maximum amount loaned of AMD 3 million (about USD 6 000).

← 3. The cost of irrigation water in Armenia ranges from AMD 17 (USD 0.04) to about AMD 30 (USD 0.073) for a cubic metre of water.

← 4. The sample size on training programmes is too small to analyse more deeply therefore.

← 5. Rerunning the regressions but excluding administrative region fixed effects suggests that the negative link between agricultural subsidies and plans to emigrate remains negative but is no longer statistically significant. On the other hand, the new set of regressions results also suggest that agricultural subsidies are positively linked with return migration (not shown), as first suggested by the descriptive statistics.

← 6. Apart from the education policies mentioned here, questions on vocational training programmes were also included in the survey, but are analysed in the labour market section.

← 7. Additional programmes not displayed in the figure due to low rate of beneficiaries in the sample (less than 1%) include: distribution of school uniforms, boarding school, inclusive and home-based education, distribution of computers to first graders, language or other catch-up classes.