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Impact of the Economic Crisis on Poverty and Social Exclusion

in the Republic of Moldova

Chişinău • 2009

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Copyright © 2009 United Nations in Moldova.All rights reserved.

Author: Thomas Manfred OtterResearch team: Sasun Tsirunyan, Maria Vremis, Viorica Craievschi-Toartă and Diana CheianuResearch Coordinator: Lovita Ramguttee, UNDP MoldovaEditing: Thomas Escritt

Opinions expressed in this Report do not necessarily reflect the official views or policies of the United Nations or any of its Agencies.

This volume is a product of the authors of the report. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the World Bank Group, the Executive Directors of the World Bank Group, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

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Executive Summary

Results

Crisis transmission channels

A combination of falling export demand and a decline in remittances from abroad are the main channels through which the crisis is being transmitted to Moldova. Combined with less (foreign and national) investment this will lead to lower production and so to higher unemployment and probably lower wages. In general, lower income leads to changes in consumption which causes substantial declines in standards of living for poorer households especially, where cuts in expendi-ture which are sensitive to social welfare (access to services, protection, basic conditions and capability-building processes) translate the economic consequence of the crisis into a social phe-nomenon.

Income

At the national level, mean per capita disposable income has fallen by -1.7 per cent and income from self employment in agriculture has fallen by -24.6 per cent. So, the initial crisis impact, understood as a fall in standards of welfare, is driven by falls in employment income and falls in self-employment income earned in agriculture, geographically concentrated in the central and southern region, in the village stratum and in rural areas, with a heightened social impact due to greater dependence on remittances, which are also falling during the crisis period in rural areas. Regarding the kind of economic activity, we observe a deeper impact on farmers and agricultural employees caused by multiple losses (employment income + self employment income + remittances) for lower quintiles.

Households with children are more vulnerable to the effects of the crisis than households without children, with one child households being the most vulnerable. Differences regarding area, strata or the employment status of household head show the same trends as for all households. Regard-ing female headed households with children, they have lower income from employment, self-em-ployment in agriculture and non-agriculture, with the family budget being compensated by higher income from remittances. These households will be more vulnerable than those run by men, if the remittances will keep decreasing.

Employment and Remittances

The usual income decrease which occurs every year towards winter time, due to the lack of agri-cultural activity, is 21 per cent greater on a national level during the crisis than in the same period before the crisis. Rural income decreased by more than 7 per cent, driven by a fall in employment in-come (12 per cent) and agricultural self-employment income (25 per cent), which together account for 47 per cent of all rural income.

Within the crisis period, urban households depend more heavily on employment income and remit-tances, while rural households depend more heavily on income from self-employment in agricul-ture, and on social payments and remittances. In the case of rural households, two of these three main income sources are decreasing (income from agriculture and remittances) and a rise in social payments alone cannot counterbalance these losses.

Household survey data confirm that remittances to urban areas, even expressed in constant prices, were still rising (up by 19 percentage points) while remittances to rural areas were already decreas-ing in early 2009 (down by 17 percentage points). Since the total amount of remittances going to rural areas is higher than the amount going to urban areas, at a national level we have a 6 per cent decrease for the total amount of remittances between January and March 2009 compared to the period between November and December of 2008.

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Income from remittances represents around 25 per cent of rural households’ total income, and their fall could have a major negative impact on rural areas. So, even with falling volumes of remittances received, 36 per cent of households who currently receive remittances and are ranked in income quintiles higher than Q1 would fall back into Q1 without remittances. The other 19 per cent would fall back from higher quintiles to Q2 without remittances. Since employment income will fall sub-stantially during the crisis period, households that receive remittances now depend more heavily on them, even if the overall volumes of remittances are declining.

According to the Labour Force Survey (LFS), in the first quarter of 2009, the ratio of persons working or seeking work outside the country was 12.7 per cent lower than in 2008.

Expenditure

Consumption expenditure of households recorded a significant decrease of 6.2 per cent in real terms at the national level. Households reduced spending on all goods and services with the exception of education, dwelling maintenance and communication expenditure. Socially the most important indica-tor we observe is a 13 per cent fall in food expenditures, a rather significant reduction considering that food expenditure represents about 40 per cent of the total household budget at a national level.

We observe a shift away from high and mid-priced staples to cheaper foodstuffs. In this way a fall in income is translated into reduced expenditure, but since the possibilities for cash expenditure cut-offs are limited, in kind food consumption is the main coping strategy for rural households, which in the long run might even have negative consequences for health conditions.

Households are forced to redirect their expenditure to cover those strictly necessary items. Re-sources are being reallocated from food consumption toward dwelling maintenance, health, and communications which can be explained by increased prices for these types of services. Regard-ing geographical distribution, across different population groups, changes in expenditure show the same trends as changes in income.

Expenditure distribution throughout quintiles, for households with children reveals a dramatic situa-tion for the households with 3 and more children. Thus, 43.7 per cent of families with three or more children during the crisis period belong to quintile 1, compared to 24.3 per cent of the families with 2 children and only 13.9 per cent of those with only one child.

Social payments and access to social services

Increased amounts of social payments went hand in hand with a fall in the number of covered households, but even for households that still do receive social payments, the observed increase is not high enough to compensate for income losses caused by the crisis. HBS data still do not make it possible to assess the impact of the most recent changes in social payments established by law No. 133. The potential benefits of this law might be limited due to unsatisfactory dissemination of information regarding the law. Some 47 per cent of households surveyed did not know anything about this law, a worrying fact considering that a mechanism of self-selection applied to these pro-grammes.

We observe an increase in the number of members of households with children that do not have com-pulsory health insurance policies – this proportion has risen from 21.8 per cent before crisis to 23.9 per cent in the crisis. Health care expenditure rose in the mean in all quintiles except for quintile III (middle class). Enrolment in pre-primary education fell by 2 percentage points during the crisis period.

Poverty

Even if we have not been able to compute final poverty rates for 2009, in the absence of an official poverty line, given that the year 2009 is still not over at the moment of writing, we have found strong

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evidence for an significant increase in poverty (additional people and households falling below the poverty line) but we have also found evidence for an even stronger crisis impact below the poverty line, making already poor people and households even poorer, leading to even greater inequality.

Community impact

This report does not concentrate on providing a detailed description of community impacts. It rather takes the view that aggregated effects impacting on households and individuals constitute the basis for an overall community effect. In order to find additional information complementary to a differ-ent special UNDP report on community effects we tried to cross-check the consumption decrease data processed for this report with the SADI deprivation index. The exercise shows that a higher con-sumption decrease is positively correlated with higher levels of income deprivation and economic and education services performance. This result gives a hint that education services (dependent on local budget execution) do perform worse in communities with a higher deprivation level. On the other hand, in the more deprived communities health services seem to perform better than educa-tion services. At the same time, communities with less demographic and housing deprivation tend to suffer higher falls in household consumption. This phenomenon could be linked to migration and receipt of remittances (in the past) which was invested in housing.

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Conclusions and RecommendationsConclusions

The report shows evidence regarding the social impact of the international crisis, which is already con-siderable, leading in the first instance to growing levels 1of rural poverty. This trend could become even more serious during 2009 and early 2010 if an economic recovery, which will start sooner or later, is not supported by strong social policies. In contrast to what has been expected for Moldova, the impact of the social crisis is not driven mainly by the loss of remittances. Our data show that employment and income loss is a much stronger crisis driver than expected, headed by a rural and agricultural depres-sion. The report compares the six month period between Q4 2008 and Q1 2009 with the same period one year before. It finds a striking 25 per cent contraction in income from self-employment in agricul-ture over this period, compared to a 2 per cent decline nationwide (“self-employment in agriculture” refers mainly to work of the many farmers who own small lots). Moreover, the report finds that food consumption in rural villages fell by 17 per cent, compared to a 6 per cent decline in the cities. It also presents evidence of a shift away from high and mid-priced staples to cheaper foodstuffs. In this way, an income loss is translated into reduced expenditure, but since the possibilities for cash expenditure cut-offs are limited, in kind food consumption is the main coping strategy for rural households, which in the long run might even have negative consequences for health conditions.

Further deterioration in poverty rates is also likely if the return of migrants continues to gather pace. In early 2009, levels of migrant return was at twice the levels of early 2008, with consequent falls in remittances and rising unemployment as those returning re-enter the domestic labour market. Given that migration has been principally from rural areas, it is expected that the effects of large numbers of returning migrants would be felt more sharply there. Nevertheless, as well for urban areas we can expect an important negative impact on employment if the number of return migrants remains high.

Access to social payments and services should be considered as a mechanism that protects against the negative impact of the crisis, but the observed scope and amount of social payments (with de-clining scope throughout the observation period of our report) are too low to compensate for the observed welfare loss. Nevertheless, full implementation of new social protection rules will reduce the impact of the crisis on the poor and ‘’new poor’’, particularly if the programme reaches all vul-nerable groups and those who are most in need are not left outside the system.

From an economic point of view we could argue that since income and employment falls causes the most important negative welfare impacts, action against the crisis’s impact should concentrate on in-come and employment generation. The link between unemployment and poverty is well established and hence sharply rising unemployment levels resulting from the domestic economic contraction are likely to have negative poverty impacts. These effects may, in practice, be exacerbated since it is less feasible now than in the past for the unemployed to rely on agriculture as an informal safety net. Consequently, policies should concentrate on employment generation. In the long run this might be possible with structural changes in the economy and in a short run it means public works and com-munity employment schemes are needed(not only infrastructure investment).

Low income quintiles in rural areas are those most affected by the crisis. This deepens poverty gaps and might increase inequality (which will make future poverty reduction more difficult). The central and southern regions are particularly affected. The household types that are most affected are fami-lies with migrants, households relying on pensions, households with a high number of children, and families with children where the household head is not the parent. Their lower income makes it hard to cover energy costs in the winter (which can itself be a health problem).

For many of the affected groups social payments fail to compensate for lost income. Even if the amount of social payments (per unit payment) increases, coverage decreases. There is the possibil-

1 Appearance of “new poor” households in rural area and of “already poor” households which are falling in an even deeper poverty.

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ity that a full implementation of Law No. 133 will marginally improve this situation, but since access to these benefits is based on household self selection, it is worrying that by the second half of 2009 only 47 per cent of all households said they were aware of the existence of the new law and the pos-sibility of requesting payments.

The available data does not enable comprehensive forecasting of poverty rates, but there is strong evidence that a substantial rise in poverty has taken place and that the impact on people below the poverty line will be much more significant than the rise in the numbers of poor people.

Recommendations

We will provide recommendations in two blocks, first regarding action that can be taken against rural poverty and unemployment and, second, action regarding social protection. Proposals are grouped according to whether they are short-, medium- or long-term solutions.

Rural poverty and unemployment

Short term:

1. Public works as a temporary safety net: especially targeted at returning migrants, youth, women and Roma. These kind of programme have a double positive effect since they pro-vide income to households and at the same time create public benefits via, for instance, in-frastructure rehabilitation, community work or implementation of environmental protection activities.

Medium Term:

2. Improve employment and social services, support youth and self-employment: • Improve coordination of services provided by the government, the private sector and

CSOs, especially at the local level;• Improve job readiness, help rural youth, women and returning migrants find work;• Address bottlenecks in the agricultural supply chain;• Lower the cost of employing young workers and encouraging business start-ups;• Micro-finance services: credit, savings, insurance.

Long Term:

3. Generate sustainable rural income:• Develop non-farm rural economy, notably agro-processing;• Provide agricultural public goods—extension services, irrigation;• Implementation of conservation agriculture.

Social protection

Short term:

4. Accelerate the roll-out of the law on Social Support by simplifying the application proce-dures, improving communication targeting the most vulnerable and monitoring implementa-tion, again focusing on the most vulnerable;

5. Transfer or incorporate the child benefit of 50 MDL into the Social Support Scheme. This would increase the amount of funds available for Social Support by 20m MDL allowing the minimum state guaranteed income threshold to rise to 430 MDL, in place of its current level below the absolute poverty line, with no impact on poverty reduction;

6. Reduce the number of the categories of people entitled for nominal compensations while keeping the following categories: families with four or more children under 18, families with

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a disabled child (excluding those with children in institutions) and adults with disabilities. Although these are politically very sensitive issues, in the current financial crisis it would be easier for the government to promote this measure. This measure should be promoted alongside a large-scale communication campaign, re-orienting the poor beneficiaries of the current nominal compensations to the social support scheme, and allowing for the level of stigmatisation surrounding applications to the social support scheme to be reduced;

7. Prevent child malnutrition by continuing the distribution of food to pregnant women, lactat-ing mothers and children under three years of age during the winter;

8. Use the results of this report to support the adjustment in means testing proposed in the government’s recovery and stabilisation plan, which envisages gradually substituting the social care system for a nominative compensation system. Means testing has to be able to target the most vulnerable groups.

Medium Term:

9. Fully implement the National Programme for the Promotion of Integrated Social Services, focusing on the development of these services in rural areas. The programme should be costed and adequate resources must be allocated. The results of the assessment show that families with many children, families with children headed by others than the parents and families with income from agriculture are most affected by the crisis. In addition, the poor are getting even poorer, which according to an analysis of previous crises that have hit coun-tries of the former USSR, if not properly addressed, might lead to a dramatic increase in the number of children in institutions;

10. Increase the coverage of the Early Childhood Development (ECD) programme, focusing on the most vulnerable children, as young children (from 0 to 7 years of age) are the most vul-nerable to the effects of the crisis, such as malnutrition, which will jeopardize their future health, development and education. International experts have proven that ECD provides the most efficient and effective return on investment;

11. Consider the possibility of implementing conditional cash transfer programmes which can target vulnerable groups temporarily throughout the crisis and recovery period, for house-holds which are not covered by social programmes and in order to ensure the process of capability creation in children continues unaffected.

Long Term:

12. Simplify the pension system, making it more transparent and linking it to contributions to the social insurance system but also providing coverage of the basic needs of elderly people, many of whom live with grandchildren left behind by migrating parents;

13. Within the medical insurance programmes, gradually raise the eligibility age ceiling for the reimbursement of medicines for the most frequent childhood illnesses, initially to 10 years old and at a later stage to 15 years old. The assessment shows that families have started to spend more on health because of the increase in the cost of medication and medical services. This increases the risk of poor families not being able to afford timely and proper treatment for their children’s illnesses, resulting in these diseases becoming chronic with further high costs for families and the whole of society;

14. Redesign the coverage and content of the basic health package for the mother and child, by emphasising the most efficient and effective medical services (e.g. immunisation etc).

15. Consider the possibility of introducing results-oriented budgeting processes for social poli-cies in the medium term, in order to effectively translate monetary resources into social outcomes.

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ContEnt1. Introduction ................................................................................................................................13

2. Antecedents .................................................................................................................................14

3. General impact of the crisis in the region .........................................................................16

3.1. Economic impact of the crisis in Moldova .................................................................17

3.2. Antecedents to social aspects of the crisis ...............................................................19

4. transmission of the international crisis into social effects .......................................20

5. Methodology and Data Sources ............................................................................................21

6. Results ...........................................................................................................................................22

6.1. Crisis transmission ...........................................................................................................22

6.2. Income effects .....................................................................................................................23

6.2.1. Income effects by area and strata – all households ...................................23

6.2.2. Income effects by area and strata - households with children ..............26

6.2.3. Employment income remittances and changes in income distribution .............................................................................................................30

6.3. Expenditure effects ...........................................................................................................39

6.3.1. Expenditure effects by area and strata – all households .........................39

6.3.2. Expenditure effects by area and strata – households with children ....42

6.4. Compensation via Social Payments and access to social services .....................46

6.4.1. Social payments .....................................................................................................46

6.4.2. Access to healthcare services ............................................................................49

6.4.3. Access to education ..............................................................................................50

6.5. Subjective assessment of the standard of life ..........................................................52

7. Poverty ..........................................................................................................................................54

8. Community impact ....................................................................................................................58

9. Conclusions and Recommendations ...................................................................................60

10. References ...................................................................................................................................69

11. Annex .............................................................................................................................................71

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List of tables, Figures, Boxes in the text

Table 1: GDP Growth projections

Table 2: Unemployment trends in Moldova 2003 - 2008

Table 3: Unemployment increase Moldova December 2008 – May 2009

Table 4: Quarterly unemployment data 2007 - 2009

Table 5: Income change and income structure by area and strata - before and in crisis (%)

Table 6: Income change by income quintiles – before and in crisis (%)

Table 7: Income change and income structure for households with children, by area – before and in crisis (%)

Table 8: Income change for households with children

Table 9: Quintile distribution of households with children by employment status of household head – before and in crisis (%)

Table 10: Number of children (up to 18) living in Moldova with at least one of their partents living abroad

Table 11: Distribution of households by per capita income quintiles with and without remittances

Table 12: Changes in income distribution of mean per capita income by area

Table 13: Access to the labour market by period (%)

Table 14: Distribution of average per capita expenditures by strata (%)

Table 15: Average per capita expenditures by income quintiles (%)

Table B3.1: Energy costs - Cumulative inflation October 2007 to March 2008 (%)

Table 16: Monthly food consumption per capita

Table B4.1: Geographical distribution of food expenditure changes – before and in crisis (%)

Table 17: Expenditure change for households with children, couples and singel parentes – befor and in crisis (%)

Table 18: Quantity of per capita food consumption, selected products

Table 19: Expenditure change and composition for households with children by income consump-tion quintiles (%)

Table 20: Quintile distribution of households with children by area – before and in crisis (%)

Table 21: Changes in average amounts and coverage of social payments by household size

Table 22: Households who receive social payments

Table 23: Average per capita monthly expenditures for health care services in households with children, by consumption quintiles and periods

Table 24: Average per capita expenditures for health care services, MDL

Table 25: Enrolment of children in educational institutions

Table 26: Average per capita monthly expenditures for education in the households with chil-dren, by consumption quintiles and periods

Table B5.1: Poverty Rates Under Alternative Scenarios of a Worker Remittances Shock

Table B5.2: Changes in Poverty Rates Under Alternative Scenarios of a Worker Remittances Shock

Table 27: Estimated poverty rates before and in crisis

Table 28: Relative changes below the poverty line

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Figure 1: Transmission of Financial and Economic Crisis into Social Effects

Figure 2: Structural changes in income sources October 2008 to March 2009 (national level)

Figure 3: Money Transfers from Abroad by Individuals (natural persons; residents and non-resi-dents) via Banks of the Republic of Moldova (in millions of US$)

Figure 4: Total level of remittances

Figure 5: Distribution of employment by economic activities

Figure 6: Distribution of some categories of households with children in quintile 1 by periods, if compared with the total means (%)

Figure 7: Distribution of some categories of households with children in quintile 5 by periods, if compared with total means (%)

Figure 8: Information of households with children about the Law on Social Aid (%)

Figure 9: Difficulties in payment for / procurement of the basic services

Figure 10: Subjective perception of household status (%)

Figure 11: Incidence of real growth, 2006-2007

Figure B5.1: Estimated Proportion of Migrants from each Quintile of Consumption Expenditures, 2007

Box 1: Production decrease

Box 2: Remittances and migration

Box 3: Energy costs

Box 4: Food expenditure

Box 5: Poverty Simulation World Bank

Box 6: Emergency employment

Box 7: Opportunity for public works action scaling up ILDP programme

Box 8: Better opportunities for women and youth project

Box 9: Support to start-ups in food processing

Box 10: Conditional Cash Transfer Programmes

Box 11: Conservations Agriculture

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Acronyms

ANOFM National Labour Insertion Agency (ANOFM)

CA Conservations Agriculture

CCT Conditional Cash Transfer Programmes

CIS Commonwealth of Independent States

EU European Union

FDI Foreign Direct Investment

GDP Gross Domestic Product

HBS Household Budget Survey

HH Household

ILO International Labour Organization

IMF International Monetary Fund

IOM International Organization for Migration

LFS Labour Force Survey

MDL Moldavian Leu

MET Ministry of Economy and Trade

NBS National Bureau of Statistics

NEA National Employment Agency

UNDP United Nations Development Programme

UNICEF United Nations Children’s Fund

USD United States Dollar

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1. IntroductionThe international crisis has hit Moldova’s economic performance. Media and other publicly available sources are full of information and comments regarding the economic impact of the crisis. Decreas-ing labour activities and a reduced receipt of remittances are quite visible consequences of the crisis. Nevertheless, a poorer economic situation has social consequences as well. Unfortunately, these so-cial consequences are frequently not as easily understandable and as visible as the economic ones. The present report tries to gather available information on the social impact of the economic crisis that can readily be quantified using empirical evidence and to envisage what social impacts may still be to come. The report also tries to give some hints on the chances for overcoming social welfare losses in the future, once after an economic recovery has begun.

Social impacts are not only diverse but affect different population groups, individuals and household types in a different manner. Additionally the same economic impact (for example an income cut of 10 per cent) can affect households or individuals with different characteristics in very divergent ways. This report looks at social impact, comparing income losses and adjustments in consump-tion expenditure, separately examining the impact on households with and without children, before looking at some general implications for overall poverty levels and aggregated impacts at the com-munity level.

The report starts by summing up the antecedents of the economic impact of the crisis in the country and the region, then presents its data sources and methodological approach, separately outlines trends in income levels and composition and expenditure levels and income composition (always separately for all households and households with children), before discussing access to social pay-ments and services as a potential means of support to protect against the impact of the crisis. Pov-erty rate predictions and some comments on community impact close the report before a discussion of the various conclusions and recommendations.

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2. AntecedentsThe international economic and financial crisis seems to be moving to an end in North America and western Europe, at least as far as production indicators are concerned, almost exactly one year after its beginning in September 2008. This is not bad news for the world; however, eastern Europe and Moldova are far from having overcome the crisis in all its dimensions. In the same way in which a financial crisis reduces production, increasing unemployment and consequently creating a negative social impact, during the recovery period, production recovers first, then employment, and only at the end does social welfare recover. Additionally there is strong evidence from across the eastern European and former Soviet Union areas that production growth in recent years has been much stronger then employment growth. There has been GDP growth but no jobs resurgence. Instead, there has been a jobs deficit alongside a growing informal sector.2 Academic research has proved that labour market recovery takes twice on long on average as a recovery in production. Considering that this might also be true for Moldova, we should expect a recovery in production in early 2010 – after an initial recovery in Western Europe – and recovery in employment in 2011 – if any –with a social recovery to come even later. These dynamics provide the most striking argument for paying close attention to the social impact of the international economic and financial crisis in Moldova, and makes it clear why policy actions are needed.

After a period of considerable growth from 2003 to 2008, which was mostly externally driven, the international crisis started to hit Moldova in late 2008. From an economic point of view, the country suffered a triple shock to its external drivers of growth: 1) A recession in the industrialised countries; 2) a severe drop in commodity prices and in the terms of trade; and 3) a growing financial vulnera-bility.3 A triple shock like this would be enough to affect any economy severely, but in the case of Moldova, remittances, which represented some 36 per cent of GDP in 2007, started to fall strongly. In other words, all the motors that pushed the country forward through the recent growth period were turned off one by one.

According to the World Economic Outlook Update published by the International Monetary Fund in October 2009, the global economy is beginning to pull out of a recession unprecedented in the post–World War II era, but stabilisation is uneven and the recovery is expected to be sluggish. In October 2009, worldwide economic growth for 2009 and 2010 was projected to be almost one percentage point higher than predictions realised in April this year.

According to the IMF, the world economy will stabilise towards the beginning of the second half of 2009. However, at a global level, the recession is not yet over and the recovery is likely to be slug-gish. Following a disappointing first quarter of 2009, during which the global economy contracted almost as fast as during the fourth quarter of 2008, recent data point to a return to modest growth at the global level in 2010. However, the advanced economies as a group are still projected not to show a sustained pickup in activity until the second half of 2010. Global activity is now forecasted to contract by 1.1 per cent in 2009 and to expand by 3.1 per cent in 2010.

However the recovery in the global economy is far from assured, with the effects of global reces-sion sure to be felt for a long time to come. The IMF warns in the report that “complacency must be avoided”, with the global recovery likely to be slow and unemployment continuing to rise for some time.

2 World Bank (2005), Enhancing Job Opportunities: Eastern Europe and the Former Soviet Union.3 The financial instability has been less strong in Moldova than in other countries of the region. It has not represented an immediate threat to the banking system. Nevertheless, credit growth slowed and more recently there seems to have been an issue with non-performing loans and a contraction in credit provision by banks, of over 50 per cent in some cases, according to some experts. Combining these increased financing difficulties with the other elements of decreased industrial production, a weaker position in terms of trade and the loss of remittances, we have a really complicated mix of effects for Moldova.

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According to the report, significant downside risks to growth remain a concern, with the IMF strong-ly advising against a premature exit from fiscal stimulus, warning that:

“[T]he main short-term risk is that the recovery will stall. Premature exit from accommodative mon-etary and fiscal policies seems a significant risk because the policy-induced rebound might be mis-taken for the beginning of a strong recovery in private demand. In general, the fragile global econo-my still seems vulnerable to a range of shocks...”

The IMF advises that “it is still too early for policymakers to relax their efforts to restore financial sec-tor health and support demand with expansionary macroeconomic policies” and that “fiscal stimu-lus needs to be sustained until the recovery is on a firm footing.”

Table 1: GDP Growth projections (%)

Projections

2007 2008 2009 2010

World output 5.1 3.1 -1.1 3.1

Advanced economies 2.7 0.8 -3.4 1.3

United States 2.0 1.1 -2.7 1.5

Euro area 2.7 0.8 -4.2 0.3

Germany 2.5 1.3 -5.3 0.3

Italy 1.6 -1.0 -5.1 0.2

United Kingdom 2.6 0.7 -4.4 0.9

Central and eastern Europe 5.4 3.0 -5.0 1.8

Commonwealth of Independent States 8.6 5.5 -6.7 2.1

Russia 8.1 5.6 -7.5 1.5

Commonwealth of Independent States – excluding Russia 9.8 5.4 -6.7 2.1

Moldova 3.0 7.2 -9.0 0.0

Source: IMF World Economic Outlook Update October 2009.

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3. General crisis impact in the regionIn the early second half of 2008, when the financial crisis had already hit the United States and was spreading towards Western Europe, there were hopes that Eastern Europe would prove relatively immune to the global financial turmoil. High commodity prices continued to drive growth in much of the Commonwealth of Independent States (CIS) region. For most of the rest of Eastern Europe, weakness in west European demand was offset by rising intra-regional trade and buoyant domestic demand, which was sustained by an abundance of credit for consumption. In this way, while west-ern European countries had already witnessed a slowdown of their economies in 2008, last year growth was extraordinarily good for many eastern European countries, among them Moldova, with an “overheated” economy, widely driven by foreign direct investment (FDI) contracts or external trade, which was continuing, even if western Europe had started already entering their sub-regional crisis period. This postponed the onset of the crisis in Eastern Europe.

But once the euro area slowdown began turning into a recession, serious consequences for east European export prospects began to emerge. Additionally, foreign capital, one of Eastern Europe’s growth motors via direct investments, was now needed in its countries of origin and entered the region in much lower volumes. As foreign capital fled and confidence evaporated, the emerging world’s stock markets plunged (in some cases losing half their value) and many currencies tumbled. The seizing up of the credit market caused havoc, as foreign banks abruptly stopped lending and stepped back from even the most basic banking services, including trade credits.

Less demand for exports, less foreign investment, lower national investments and less consumption within countries resulted in a sharp fall in production. And less production means lower employment and fewer income generation opportunities: less work means less income, so negative social impacts can be expected. Social impacts are mainly driven by income losses and shifts in consumption ex-penditure, such as less expenditure on food, education or health carried out by the households. This leads to different kinds of social impact, which must be identified, and as will the extent to which they depend on the degree of income loss, and household decisions on how to deal with this situa-tion and anti-crisis measurements implemented by the government must also be examined.

For many Western European countries, this in essence captures more or less the whole picture regarding the crisis impact. Unfortunately, for many eastern European countries in general and for Moldova in particular, there is an additional element that has major economic and social implica-tions – the issue of migration and remittances receipts.

As economies turn down, migrants suffer. Many industries where migrants cluster (tourism in Ireland, financial services in Britain, construction in America and Spain) have shed jobs fast. Staying abroad does not seem to be an attractive prospect for many of them under the given circumstances, but re-turning home seems even gloomier, because there are limited or no job prospects back home either.

The employment problem –and closely linked to it the problem of the negative social impacts of the crisis – seem to be the most striking challenge for development policies throughout the world for many years to come. The world economy faces its biggest rise in unemployment in decades.4 An im-

4 According to the ILO world wide labour market projections for 2009 from May 2009, this year will represent the worst global performance on record in terms of employment creation. The report underlined that the global labour force is expanding at an average rate of 1.6 per cent, equivalent to around 45 million new entrants annually, while global em-ployment growth decreased to 1.4 per cent in 2008 and is expected to drop further to between 0 and 1 per cent in 2009 “We are seeing an unprecedented increase in unemployment and the number of workers at risk of falling into poverty around the world this year”, Mr. Somavia said. “This is cause for grave concern. To avoid a global social recession we need a global jobs pact to address this crisis, and mitigate its effects on people. The choice is ours and the time to act is now.” ILO Director-General Juan Somavia, June 2009. Mr. Somavia cautioned that past experience suggested a consider-able lag of 4 to 5 years on average in the recovery in labour markets after economic recovery. There was a risk of the global jobs crisis “persisting” for the next several years.

17

portant question is how governments will react and shape labour markets over the next years. Even if throughout the second half of 2009, the crisis impact on the financial sector seems to be under control and there are some signs of recovery in production, the next phase of the world’s economic downturn is taking shape: a global jobs crisis.

But government reactions to address the crisis and the job losses require resources and fiscal and political space and capacity for action. All three of these are limited due to the crisis. For countries that have benefited in recent years from high inflows from external sources, delivered by a highly leveraged global financial system, this combination creates additional challenges. The significant current account deficits in most of the eastern European countries (among them Moldova, with a current account deficit in 2008 of 19 per cent and a forecast mean current account deficit of 11 per cent for 2009 and 2010) suggest they may be living beyond their means.

Additionally, in the emerging world, many economies have been hit by the sudden fall in private cross-border capital flows. Emerging economies, which imported capital worth 5 per cent of their GDP in 2007, now face a world where cautious investors keep their money at home. According to the IMF, banks, firms and governments in the emerging world have some $1.8 trillion worth of bor-rowing to roll over this year, much of that in central and Eastern Europe. Even if emerging markets escape a full-blown debt crisis, investor confidence is unlikely to recover for years.

So many eastern European countries have limited financial room for manoeuvre within the crisis recovery period starting in the second half of 2010. The experience of the global economic depres-sion in the 1930s showed how damaging it can be if governments fail to step in when the rest of the economy has already started a new period of expansion.

3.1. Economic impact of the crisis in Moldova

In June 2009 the IMF characterized Moldova’s macro economic situation as follows: “Like most coun-tries in the region, Moldova has been hit hard by the global economic crisis. Real GDP is projected to shrink by at least 9 per cent in 2009, and there are worrying signs of deflation. Export markets have shrunk and foreign direct investments and remittances have fallen sharply. The current account deficit is expected to remain large despite the rapid import contraction. A large external financing shortfall is emerging due to low capital inflows.”5

Several indicators already available regarding Moldova’s economic performance throughout 2009 show a clear downturn.6 GDP declined in the first half of 2009 by 7.8 per cent compared with the first quarter of 2008. The crisis’s impact on different production sectors is not uniform. The produc-tion downturn in industry is higher than in trade and agriculture. Also, export-oriented activities are affected more seriously than domestically-oriented ones. Nevertheless, as will be shown below, em-ployment and income effects in consequence of the downturn in production levels make for a social impact out of proportion to the mere amount of sector GDP decrease.

The main contagion channels which transmit the effects of the financial and economic crisis to Moldova are falling remittances, in consequence of declining domestic and external demand, a re-duction in the foreign direct investment inflows, which simultaneously reduces employment oppor-tunities and increase unemployment (deepened additionally by returning migrants) and shrinking governmental transfers to local budgets.

Shrinking employment opportunities in consequence of the crisis will worsen an already existing trend in the country. Since the late 1990s, unemployment has been declining steadily and, in 2007, was just over 5 per cent and fell further to 4 per cent in 2008.

5 IMF Press Release No. 09/206: June 10, 2009. 6 Information on economic impact in Moldova extracted from UNDP/Expert-Grup report on “Impact of global financial crisis on local communities in Moldova”, August 2009.

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Table 2: Unemployment trends in Moldova 2003 - 2008

YearNumber of unemployed (1000) Unemployment rate (%)

Total Female Male Total Female Male2008 51.7 21.7 30.0 4.0 3.4 4.62007 66.7 25.2 41.5 5.1 3.9 6.32006 99.9 38.2 61.7 7.4 5.7 8.92005 103.7 43.9 59.8 7.3 6.0 8.72004 116.5 46.4 70.1 8.1 6.3 10.02003 117.1 47.2 69.9 7.9 6.4 9.6

Source: National Bureau of Statistics

The resumption of economic growth in 2000 was not accompanied by an increase in employment and, instead, employment in Moldova has also declined. This trend has been mainly the result of emigration - about 340,0007 Moldovans out of 3.6 million (not counting Transnistria) are currently working abroad.

The impact of the global and economic crisis has already taken a toll on the labour market in the country through various channels, with huge implications for unemployment in the country, adding returning migrants (according to the Labour Force Survey from National Bureau of Statistics, 16,000 migrants returned in the last quarter of 2008) to the newly unemployed who lost their jobs inside the country. It is expected that some 100,000 to 150,000 Moldovans working abroad could return home over 2009, especially from the Russian Federation (Viitorul Institute for Development and Social Initiatives predictions).

The implications of the above-mentioned trends on the labour market are already being reflected in official registered unemployment statistics by the National Labour Insertion Agency (ANOFM), as indicated in the table below (while the data only reflect those who have been registered with the agency, it is highly indicative of the trend):

Table 3: Unemployment increase Moldova December 2008 – May 2009

Dec 08 Jan 09 Feb 09 Mar 09 April 09 May 09

Number of jobless people registered at the beginning of the month (01)

17,416 17,833 23,204 30,060 35,440 35,851

Jobless people registered throughout the month (02)

4,193 8,052 10,257 9,836 5,493 5,855

Jobless people employed during the month (03)

1,757 1,094 1,442 1,604 1,201 1,482

Jobless people taken out of record throughout the month (04)

2,019 1,587 1,959 2,852 3,881 4,348

Number of the jobless recorded at the end of the month, (01+02-03-04)

17,833 23,204 30,060 35,440 35,851 35,876

Of whom: - dismissed 2,425 3,077 3,689 4,021 4,073 4,080

Source: ANOFM

7 Data from NBS labour force survey. According to estimates regarding the stock of migrants living abroad, carried out by the World Bank, the total number of Moldovans (of all ages) living abroad is almost 700,000 people (Ratha and Shaw (2007) Bilateral Estimates of Migrants Stocks Database, South-South Migration and Remittances. World Bank, Washing-ton, D.C.). The difference between these two data sets is that people registered by LFS are labour migrants who might or might not remittances to the country, while the estimates regarding the total stock of migrants includes any person from Moldova who did migrate in any given moment of his live, permanently or temporarily, whether sending back remittances or not.

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The table indicates a continuous increase in the number of jobless people registered and recorded since December 2008 – a near twofold increase between December 2008 and March 2009 for those recorded at the end of month; a more than twofold increase during the same period for those reg-istered as unemployed throughout the month.

There is still not enough statistical evidence available on migration flows and their motivation, to be able to quantify their real flow and drivers. An IOM study from early 20098 suggests that for migrants who had returned to Moldova by the end of March 2009, family reasons (for example that a fam-ily member got sick) were the main reason for permanent (48.3 per cent) and temporary (57.5 per cent) return. In comparison, having lost a job abroad was declared as the main reason only by 16.3 per cent (permanent) and 8.9 per cent (temporary) of returnees respectively.

This indicative trend in motivations brings the IOM analysis close to the social impact of our study, where important losses from national income sources (all non-remittances) and associated decreases in expenditure – as will be shown below - could very well cause a mix of economic and social problems which motivates relatives living abroad to come back home at least temporarily, additionally at a mo-ment when opportunities for employment and income generation abroad are as well reduced.

3.2. Antecedents to the social aspects of the crisis

Several studies so far have dealt with the issues and early evidence regarding the impact of the social crisis in Moldova on a more macro level, many of them focusing on the fall of remittances as one of the main expected drivers for the social impact of the international crisis.

For example, the World Bank concludes in a recent report9 that the distribution of consumption in Moldova implies that changes in workers’ remittances, migration, and energy prices could influence consumption and poverty rates in some unexpected and even counter-intuitive ways. Relatively well-off groups, rather than the poor, benefit most from remittances and have the most to lose from a decline, and migrants come disproportionately from relatively rich households, and this is true even after adjustment for income received from remittances. This determines who benefits and gains from shifts in the flow of remittances.

An IOM study10 suggests that the migration experience at the household level is more widespread and volatile than the total number of migrants at a given point in time might suggest. So there is a threat of an even further decline in remittances, which has been one of the drivers of growth in recent years.

Another IOM study11 confirms the existence of a low level of migrant and remittance participation in the business environment and banking system, as well as a continuous worsening of the entrepre-neurial and general economic context, concluding that the recent past is marked by a decay in the population’s living standards, accompanied by an increase in unemployment and a decrease in the ability of households to make savings, as well as a reduction in the extremely negative assessments of our country as a place to live, work, and make investments, which basically limits the possibility for a quick recovery driven by our own Moldovan forces once the crisis is over.

The World Bank, in line with other agencies (IOM, UNDP, UNICEF, ILO), concludes its study by stress-ing that the policy challenge is to mitigate the immediate shocks in rural areas and among youth and children and to limit the negative effects on poverty. The short-term policy response would be first to avoid cutting the targeted social assistance program. Second, public works could absorb skilled construction workers while a youth program could mobilise and invest in unemployed youth.

8 IOM: The Socio-Economic Impact of the Economic Crisis on Migration and Remittances in the Republic of Moldova, April 2009.9 World Bank, 2009, MOLDOVA: The Consequences of Several Shocks for Consumption and Poverty Report No. 49019-MD10 IOM/CBS-AXA, 2008, Labor Migration and Remittances in Moldova, 20062008: Trends and Preliminary Findings from the Panel Household Survey11 IOM, 2009, The Socio-Economic Impact of the Economic Crisis on Migration and Remittances in the Republic of Mol-dova Early Findings – Spring 2009

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4. transmission of international crisis into social effectsFrom a structural point of view, the channels by which the financial and economic crisis affects people and their families, in turn leading to social problems, are outlined in Figure 1. In general, an initial fall in demand for exports (which additionally can be deepened through a fall in the other currencies of the region) further drags down output levels and income (of the national economy). If this occurs, it leads to further drops in employment and wages. Lower output additionally leads to lower government revenues, which in turn negatively affects government budgets, including social sector programs. Unemployment reduces wages and possible price rises could place a strain on vul-nerable groups and their family members, which is exactly what is happening in Moldova regarding energy prices and education costs. Meanwhile some other prices might fall due to a general absence of inflation. Insufficient social safety nets cannot offset these effects and further add more tension, and even lead, in some cases, to family breakdown and create additional social problems like family disintegration (even if there is still no statistical evidence available for Moldova which can show if this kind of family disintegration is happening or not).

Figure 1: Transmission of Financial and Economic Crisis into Social Effects

Source: Own elaboration by the report team

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5. Methodology and Data SourcesThe following analysis is mainly based on descriptive statistics, processing data from the House-hold Budget Surveys (HBS) of the National Bureau of Statistics (NBS), and the Social Exclusion Module, which is not sufficiently analysed and needs to be adopted by the Moldovan authorities, covering the period from last quarter of 2007 to the first quarter of 2009. Methodologically we are comparing a period before the crisis with a period already affected by the crisis. In order to be able to avoid data bias due to seasonal effects we added up the months corresponding to the last quarter of one year and the first quarter of the following year and built up to periods for compari-son. Period one includes the last quarter of 2007 and the first quarter of 2008, denominated in our exercise as the pre-crisis period. Period two includes the last quarter of 2008 and the first quarter of 2009, denominated as crisis period. The main step of our exercise is the comparison of per-centage changes in different income and expenditure types between the pre-crisis and the crisis period. For this exercise all prices were deflated to October 2007 values. The last quarter of 2008 realistically seen does not yet reflect a statistically significant social crisis impact, which starts to deepen in January 2009 as can be seen by monthly comparisons (for example comparing January 2008 with January 2009). Nevertheless, we kept the periods as described above, because compar-ing alternatively for example the last quarter of one year with the first quarter of the following one would not give clear crisis impact results, since many incomes (for example from agriculture) always go down in this stage of the year.

Declines shown below for rural areas would be overstated, since we used a national price index (no rural price index has been processed and is available) to deflate nominal agricultural incomes and nominal rural expenditures.

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6. Results6.1. Crisis transmission

Figure 1 showed how falling demand for export products can be considered not just the starting impulse but also the driver of an economic crisis and how this initial impulse spreads out simul-taneously across production, trade and investment. Lower economic activity then means less em-ployment or income generation activities, possibly lower wages and reduced government revenues. These phenomena can be observed in Moldova during the year 2009.

Throughout the first semester of 2009, according to the NBS, foreign trade activity in the first half of the year shows a 32.2 per cent reduction in commercial exchanges, down to 2.1 million USD. Faced with a decrease in consumption in the main export markets for Moldovan goods and with less production ac-tivities, exports fell 20.7 per cent to 580.8 million USD. Imports have plummeted because of dwindling demand on the local market. Foreign direct investment (FDI) attracted into the Moldovan economy in the first quarter of this year fell by $80 million compared with the corresponding period last year, to $48.6 million or 4 per cent of GDP. In consequence, throughout the second trimester of 2009, industry registered a sharp drop of 25.5 per cent. Within this general category, the extracting industry fell by 31.1 per cent. In consequence of the sharp decline in economic activity in the first half of 2009, the national budget collected 15.1 per cent less money than in January-June 2008. Internal demand (final consumption and capital formation) fell by 19.2 per cent, mainly due to the 36.7 per cent reduction in fixed capital formation and an 8.2 per cent drop in final consumption. The fall in final consumption is driven in part by reduced consumption by private households, many of whom were affected by unem-ployment, which increased sharply throughout the first trimester of 2009. Between the last trimester of 2007 and the first trimester of 2008, unemployment increased from 5.4 to 5.5 per cent, while the activity rate decreased from 42.6 per cent to 41.7 per cent. One year later, between the last trimester of 2008 and the first one of 2009, the unemployment rate rose from 3.9 per cent to 7.7 per cent, while the activity rate fell from 41.8 per cent to 39.9 per cent. The total number of unemployed people rose from 67,000 to 91,000 people between the first quarter of 2008 and the first quarter of 2009. The de-crease in remittances will be analysed in more detail below.

Table 4: Quarterly unemployment data 2007 - 2009

Economically active

populationEmployed Unemployed Activity rate

Occupation rate

Unemploy-ment rate

In Thousands %Q4 2007 1250.0 1183.0 66.9 42.6 40.3 5.4Q1 2008 1221.0 1153.6 67.4 41.7 39.4 5.5Q2 2008 1399.2 1356.7 42.6 47.5 46.1 3.0Q3 2008 1358.7 1310.0 48.7 46.1 44.5 3.6Q4 2008 1232.3 1183.9 48.3 41.8 40.2 3.9Q1 2009 1181.0 1090.1 90.9 39.9 36.8 7.7Q2 2009 1358.1 1275.3 82.8 45.9 43.1 6.1

Source: NBS

Changes in employment levels imply changes in income. The lower the household income, the fewer are the possibilities for adjustments in expenditure. Expenditure cuts on sensitive purchases like food (linked to health condition), heating and procurement of health or education services can quickly lead to important social impacts on households, since coping strategies for households are limited. At the same time, the state also suffers a severe loss of revenues, which limits its ability to offset negative effects on households by increasing social expenditure.

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In order better to visualise the differentiated social impacts, we present the results of the social crisis impact or its vulnerability to social impacts, separating out household income and expenditure. At first we present the results for all households and then the results for households with children.

6.2. Income effects

6.2.1. Income effects by area and strata – all households

Table 5: Income change and income structure by are and strata - before and in crisis (%)

National Urban Rural / Villages Cities Towns

Income change

Income struc-ture in crisis

period

Income change


period

Income change


period

Income change


period

Income change


periodDisposable income per capita

-1.7 100.0 3.4 100.0 -7.3 100.0 3.2 100.0 5.1 100.0

Income from employment

1.8 43.5 8.1 57.6 -11.9 26.5 11.0 64.1 3.4 45.4

Income from self-employment in agriculture

-24.6 9.9 -4.4 1.1 -25.5 20.5 -22.1 0.1 -5.3 3.1

Income from self-employment in non-agriculture

8.7 7.2 3.1 7.9 18.1 6.4 5.6 7.7 -0.3 8.3

Social payments 1.8 14.9 -2.4 12.1 5.4 18.2 -1.8 10.3 -3.3 15.5Remittances 13.0 19.6 29.5 15.4 3.2 24.6 28.5 11.8 29.7 22.1Other incomes -0.3 4.9 -4.0 5.9 3.1 3.8 -16.1 6.0 17.1 5.6

Source: NBS; HBS IV 2008 and I 2009; self processed data.Obs: In 2007 Oct prices.

At the national level disposable mean per capita income decreased by 1.7 per cent after the onset of the crisis (up until the end of March 2009). Considering nationwide mean values this decrease is mainly driven by a strong – 24.6 per cent – fall in income from self employment in agriculture, explained by climatic effects such as floods, and as a result of the delayed effects of the drought of 2007. Nevertheless, we can also consider that the decline in agricultural income was more likely caused by falling domestic demand and the resulting fall in farm-gate agricultural prices.

The strong fall in self-employment agricultural income does not have a deep impact at national level, given that it only represents 10 per cent of disposable income per capita, but of course it has a much stronger effect where it accounts for a higher part of household income, as for example in rural areas (see below).

Between the two periods, a redistribution of household income structure can be observed. A signifi-cant decrease in the share of income from employment and self-employment in agriculture takes place at the same time as an increase in revenues from self-employment in the non-agricultural sector and income from other sources, including remittances. At a first glance, the remittances increase appears contradictory, given the decreasing absolute values of remittances. However, it is important to bear in mind that reference is made currently to percentage changes in national means. In the sections that follow, different disaggregated groups who are seeing remittance volumes fall will be identified.

The scope of crisis impact differs by area of residence. The impact is lower in urban areas and con-siderably higher in rural ones. Rural incomes were significantly influenced by weather and external

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shocks, including the prevalence of imports over exports (trade deficit recorded: export - USD 595.3 million versus imports - 631.3 million USD12). This situation, combined with the resulting stagnant remittances13 has led to significant reductions in their income.

The centre (30 per cent of all households) and southern (20 per cent of all households) regions are the regions most affected by the crisis, with income decreases of 7 per cent (centre) and 6 per cent (south). In both regions the income decrease is driven by a fall in employment income (11 per cent and 5 per cent) which accounts for 31 per cent (centre) and 35 per cent (south) of all available income. Addition-ally, self-employment agricultural losses are important in the central (22 per cent), northern (19 per cent, 32 per cent of all households) and southern regions, considering that they account for 14 per cent (north), 18 per cent (centre) and 13 per cent (south) of all disposable income.14

Regarding size, households with 3 persons and 5 and more persons are the most affected by the crisis (income losses of 5 per cent and 2 per cent respectively). It is important to note that crisis transmission channels are quite different for these two population groups. Extended households (5 and more peo-ple) lose per capita income due to employment and self-employment income decreases (accounting for 55 per cent of their per capita income), while 3-person households suffer mainly remittances losses (12 per cent decrease in remittances which account for 20 per cent of total per capita income; agri-culture self employment income only accounts for 8 per cent of total per capita income so its 26 per cent loss has less significance). Surprisingly, we observe a 12 per cent loss in social payments income for 4-person households (social payments account for 5 per cent of per capita income in these house-holds. Table 21 below shows that even if for many payment schemes the mean amounts of payment have increased, there is a significant decrease in coverage, mainly affecting four-person households (counting only people living in the household in Moldova, excluding family members living abroad).

The group of households consisting of 1-2 people are mostly from rural areas (63.1 per cent), most of them without children (94.4 per cent) and more than half of their people over 60 years old (51.8 per cent). This group is mostly affected by income losses from agricultural activities.

The group of three-person households are mostly households with one child (42.6 per cent) or with-out children (46.7 per cent) aged between 30-59 years (75.4 per cent of their household members). Many of these households lost income from remittances or employment income.

The group of four-people households are those with children (about 74 per cent), including family couples with children (about 33 per cent) or other households with children (40 per cent), which recorded a significant increase in remittance income (around 20 per cent) in total revenues.

Large households (with five and more members) belong mostly to the group “other households with children”. These are households with children headed by persons other than parents, where two or more family couples live together, mostly headed by pensioners, and younger couples with low wages due to lack of experience and skills, or students, with children aged up to 16 years. This group also includes households with children under the guardianship of grandfathers, relatives, and fami-lies with adopted children. According to HBS data in this group of households a high risk of poverty (31.6 per cent) is observed, constituting 38.9 per cent of poor people15. Loss of employment income in these households may be explained by the fact that for this group the risk of losing a job or an in-come is higher, and the loss of income from agriculture is a consequence of the lack of opportunities for selling their own products. It needs to be noted that even the growth of remittances (36.7 per cent) for this group can not cover their per capita income decrease (1.7 per cent).16

12 IDIS (2009). Economic monitor no. 15. Q 1, p. 2513 Stagnant remittances regarding mean levels according to household survey but falling overall amounts of remittances according to Central Bank data. See figures 3 and 5 in the text.14 For statistical data see annex.15 Raportul privind Sărăcia şi Impactul Politicilor, 2007, MEC16 For more details on this section please refer to Table A1 in the annex.

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In general it does not seem that female-headed households are worse off in the crisis, compared to male-headed ones, but it may be that single-headed households with children that are worse off are mainly female-headed. Female-headed households lost income from self-employment both in agricultural (34.8 per cent) and non-agricultural sectors (23.3 per cent), which were recovered from social payments (15.9 per cent) and remittances (10.6 per cent). Their income represents 18.3 per cent and 25.5 per cent of total household income respectively. Male-headed households lost im-portant amounts of income from employment and self-employment in agriculture and these losses cannot be recovered by increased revenue from remittances.17

The analysis of the situation of households by socio-economical status reflects the same trends. Most affected by the crisis are the households headed by farmers (28.4 per cent), which are those whose income is from self-employment in agriculture. These households saw falls in almost all their income sources. Regarding economic activity, farmers’ households are those most affected by the crisis, which in additional to lost employment income also loses remittances (45 per cent and 17 per cent of the farmer’s per capita income). Households headed by employees in the agricultural sector are also affected by the crisis by loss of their income from employment and self-employment. For this category of households remittances are stagnant, registering a growth of only 1.8 per cent.18

Table 6: Income change by income quintiles – before and in crisis (%)

Q 1 (poorest) Q 2 Q 3 Q 4 Q 5 (richest)

Income change


period

Income change


period

Income change


period

Income change


period

Income change


periodDisposable income per capita

-17.7 100.0 -10.5 100.0 -4.5 100.0 2.3 100.0 4.3 100.0


-15.8 27.2 -8.4 31.9 -11.7 35.2 10.3 48.5 7.5 48.3


-19.4 31.2 -7.8 19.5 -32.4 13.4 -33.0 9.3 -23.9 4.4


69.5 3.2 -9.1 4.4 -2.8 5.6 38.6 9.2 2.2 7.8

Social payments -24.0 21.8 -6.7 29.8 23.9 29.0 -2.5 13.5 -0.4 6.6

Remittances -19.9 10.7 -28.7 9.5 18.1 13.1 18.2 15.9 22.7 27.0

Other incomes -17.0 5.8 -23.3 4.8 6.1 3.8 -1.2 4.6 6.8 5.8


Most affected by economical crisis are households placed in the bottom disposable income quin-tile. Households from the first to third quintiles are predominantly affected by loss of income from employment and self-employment in agriculture. In addition, households from quintiles one and two lose significant income from remittances, and households in quintiles two and three also lose income from self-employment in the non-agricultural sector. But the crisis also affects households in higher quintiles, which lose income from self-employment in agriculture, which is an insignificant share in their income, and is compensated for by the growth of remittances. Remittances represent

17 For more statistical details of this section please check Table A2 in the annex.18 For more statistical details of this section please check Table A3 in the annex.

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16 per cent of income for households in the fourth quintile and 27 per cent for households in the fifth quintile.19

It is important to note that the first quintile is represented by a large share of households headed by farmers and workers in agriculture; they represent about 50 per cent of the total. Households from the second and third quintiles are mainly headed by pensioners – more than 50 per cent of them – and quintiles four and five by employees in the non-agricultural sector – about38 per cent and 51 per cent of those quintiles.

It should be noted that the share of households without children and those with migrant members abroad are located mostly in the higher quintiles.

Distribution of households by quintiles and the residence areas shows that households in large cities are included in high quintiles and the households from the rural areas in lower Quintiles.

6.2.2. Income effects by area and stratum - households with children

The world crisis did not uniformly affect the population of the Republic of Moldova. According to HBS data, after the outbreak of the financial and economic crisis, the average monthly per capita income fell in MDL terms in households with children.

Table 7: Income change and income structure for households with children, by area – before and during the crisis (%)

National Urban Rural Cities TownsHouseholds

with children up to 18

Households without children

Households with children up to 18

Income change


Income change


Income change


Income change


Income change


Income change


Disposable income per capita

-1.8 100.0 0.0 100.0 4.1 100.0 -8.5 100.0 3.9 100.0 4.2 100.0


-0.9 45.3 6.5 41.5 3.5 57.3 -17.9 27.1 7.1 67.9 0.5 48.2

Income from self-employ-ment in agriculture

-25.6 10.1 -22.2 9.8 -14.4 1.3 -24.5 20.3 0.0 0.1 -26.7 2.4

Income from self-employ-ment in non-agriculture

1.3 8.8 21.4 5.4 -22.7 8.4 29.8 8.4 -5.2 10.0 -37.5 7.1

Social payments

6.3 7.4 5.0 23.5 7.2 6.7 8.1 9.2 -4.7 4.2 17.4 8.9

Remittances 10.0 23.9 16.5 14.6 31.6 20.9 1.3 31.6 21.3 11.4 40.4 28.9Other incomes 5.3 4.6 -7.9 5.3 16.1 5.3 0.8 3.3 0.4 6.7 19.4 4.4


First of all, households with children suffer income losses compared to households without children, whose per capita income remains the same.

19 For more statistical details of this section see Table A3 in the annex.

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Analysis of income sources reveals that in the case of households with children, incomes from self-employment in agriculture fell, while the other types of incomes, including remittances, rose. In the case of families with children, the income from self-employment in agriculture fell by 25.6 per cent.

Analysis of the income of families with children by living environment reveals that families with children from rural localities are more vulnerable. Their income fell by 8.5 per cent; in urban area it increased by 4.1 per cent. Households with children from rural localities were more seriously affected by the loss of income from employment and self-employment in agriculture20. This is attributable to economic reasons. A comparison of the situation of households with children by type of localities (cities, towns or villages) reveals that the least affected are households with children from cities, followed by towns, with villages coming last. In towns, the incomes from self-employment in non-agriculture decreased (by 37.5 per cent), but the income from remittances increased (by 40.4 per cent, respectively. The average income per person did not fall. It is also relevant households with children from villages and towns are more dependent on remittances, with their share accounting for one third of the average per capita income, compared with one tenth in the cities during in the crisis.

In regional terms, households with children from the southern region of Moldova have the lowest incomes, both before the crisis and during the crisis, followed by the central region, with the north-ern region having the highest incomes.

The more members a household with children has, the lower its per capita income. Respectively, the per capita income in households consisting of one to two members are twice as large as the per capita income of households consisting of five or more members, amounting respectively to MDL 1382 and MDL 700 per capita per month during the crisis period. Families with three or more chil-dren have the lowest per capita incomes, standing at MDL 603 during the crisis period21.

According to the HBS data, 50 per cent of families with children live in extended families, 41 per cent of such families are couples, and about 9 per cent are single-parent families. The financial and economic crisis has affected these types of households in different ways. Thus, for the couples with children, we observed a fall in the average monthly per capita income, resulting from an 11 per cent fall in employment income and a 24.8 per cent fall in income deriving from self-employ-ment in agriculture. In case of single parents, the average per capita income increased by 29.2 per cent as a result of rises in other sources of income, including income from employment (by 30.6 per cent), in spite of the sharp decline of the income from self-employment in non-agriculture (which fell by 47.0 per cent) and self-employment in agriculture (which fell 70 per cent). For the extended families the average per capita income decreased insignificantly. The fall was caused by falling income from self-employment in agriculture (a 25.2 per cent fall) and in non-agriculture (down 28.1 per cent), which was compensated for by an increase in employment income (up 10.9 per cent).

Remittances have an important role in the total income of extended families and single-parent fami-lies, with a share of about 30 and 26 per cent respectively, compared with 17 per cent in the case of family couples (all crisis period figures).

20 See the annexes and Table 2. Monthly average per capita income by period and urban/rural area.21 See the annexes, Table 3. Monthly average per capita income by period and number of children up to 18.

28

Table 8: Income change for households with children

Couples with children up to 18

Single parent with children up to 18

Other HH with children up to 18

Income change

Income structure in crisis

Income change


Income change


Disposable incomes per capita -3.5 100.0 4.7 100.0 -0.8 100.0Income from employment -11.0 52.5 30.6 29.7 10.9 40.7Income from self-employment in agriculture

-24.8 8.6 -47.0 5.4 -25.2 11.9


47.2 13.3 -70.0 4.9 -28.1 5..3

Income from property 0.0 0.4 214.3 2.3 100.0 0.2Social payments -23.3 3.4 15.9 9.7 17.9 10.5 pensions -30.0 1.4 19.4 8.2 13.6 7.7 child allowances 11.1 1.0 -25.0 0.3 50.0 1.0 family compensations 0.0 0.3 0.0 0.4 25.0 0.6Other incomes 12.6 21.7 29.2 48.0 -1.1 31.5 remittances 33.9 17.3 -2.7 26.0 2.0 29.4

Source: NBS; HBS IV 2008 and I 2009; self-processed data.Obs: In 2007 Oct prices.

Household’s welfare depends on the education level of the household head. In general, the higher the educational level, the lower the vulnerability of the household is, including to the economic crisis. Thus, the average monthly per capita income in households whose head has higher education rose to MDL 1515 (by MDL 101), compared with households, whose head has primary or secondary educa-tion, where the average monthly per capital income fell to MDL 619 (by MDL 58)22. Respectively, only 6 per cent of households run by people with higher education belong to quintile one, compared with the 35 per cent of households whose head has primary or secondary education during the crisis period.

Analysing the sources of income in households with children by the gender of the household head reveals some issues worth noting. Households run by women have lower income from employment, self-employment in agriculture and non-agriculture, with the family budget being compensated for by higher income from remittances23 As a result, these households will be more vulnerable than those run by men, if the remittances fall.

The occupational status of the household head is an essential factor, determining the welfare of families with children. The HBS data indicate that the crisis has significantly decreased the incomes of households run by farmers, employees in the agricultural sector and entrepreneurs. Analysis of the income of households with children by the social and economic status of the household head and by quintiles during the period before the crisis and during the crisis revealed important changes. The number of farmers and employees in the agricultural sector in the fifth quintile fell, increasing their number in the poorer quintiles. Thus, after the outbreak of the economic crisis, 63 per cent of farmers and 40 per cent of employees from the agricultural sector belong to the lower quintile, according to their income (53.2 per cent and 34.3 per cent respectively before the crisis). At the same time, there is a trend of entrepreneurs passing from the highest quintile into lower ones. The number of entrepreneurs from quintile five decreased from 91 per cent to 26.8 per cent.

22 See the annexes, Table 4. Monthly average per capita income by period and education level of household head.23 This phenomenon is most probably caused by an absent spouse sending remittances, since most of the (temporarily) female-headed households have spouses abroad. See the annexes, Table 5. Monthly average per capita income by pe-riod and gender of household head).

29

Table 9: Quintile distribution of households with children by employment status of household head – before and in crisis (%)

Employment status of HH head Period Q I

(poorest) Q II Q III Q IV Q V (richest) Total

Farmers Before crisis 53.2 16.6 9.0 11.9 9.4 100

In crisis 63.0 20.5 8.9 4.3 3.3 100

Employees in agricultural sector

Before crisis 34.3 22.2 14.7 13.3 15.6 100

In crisis 40.0 18.5 17.7 19.2 4.6 100

Employees in non-agricultural sector

Before crisis 10.5 17.8 21.0 24.4 26.5 100

In crisis 11.3 16.6 15.8 27.6 28.9 100

Self-employeers Before crisis 0.0 9.1 0.0 0.0 91.0 100

In crisis 14.4 0.0 6.8 52.0 26.8 100

Pensioners Before crisis 31.9 27.2 21.5 15.2 4.4 100

In crisis 25.2 26.2 27.3 17.0 4.4 100

Other Before crisis 18.4 16.4 20.1 21.2 23.9 100

In crisis 15.8 19.2 20.3 23.1 21.7 100

Entire sample Before crisis 24.3 19.3 18.3 19.2 18.9 100

In crisis 23.5 19.4 17.7 21.4 18.1 100

Source: NBS; HBS IV 2008 and I 2009; self processed data; Obs: In 2007 Oct prices.

Migration is an important phenomenon for the analysis of crisis’s impact on households with chil-dren. According to the HBS data, during the period before the crisis 20.1 per cent (441) children lived without one or both parents as they were working abroad, and during the crisis period their number decreased insignificantly to 19.9 per cent (405) of children. Thus, according to the HBS data we find out that due to the crisis, people working abroad, especially mothers, lost their jobs (a fall of around 1.1 percentage points).

Table 10: Number of children (up to 18) living in Moldova with at least one of their partents living abroad

Before crisis,Total children in the sample - 2196

In crisis,Total children in the sample - 2038

No. of children whose

parents are migrants

The share of children

whose parents are migrants, by categories,

%

Share in the total sample,

%

No. of children whose

parents are migrants

The share of children

whose parents are migrants, by categories,

%

Share in the total sample,

%

441 100 20.1 405 100 19.9

Mother work abroad, father in HH

134 30.4 6.1 101 24.9 5.0

Father work abroad, mother in HH

267 60.5 12.2 263 64.9 12.9

Both parents work abroad

41 9.1 1.9 41 10.1 2.0


30

Remittances have an important role in the eradication of child poverty.24 The share of households with children without migrant parent(s) in the lower quintiles is twice as high (26.2 per cent in quin-tile one and 21.8 per cent in quintile two) as the share of children with migrant parent(s) (13.1 per cent in quintile one and 13.5 per cent in quintile two) during the crisis period. The number of house-holds with children with migrant parent(s) in the lower quintiles decreased during the crisis period by 6 percentage points in quintile one and over 4 percentage points in quintile two.

Summary income analysis

At the national level, there was a fall in mean disposable per capita income of 1.7 per cent with a strong fall in income from self employment in agriculture of 24.6%. So the initial crisis impact understood as a welfare loss is driven by employment income losses and losses of income from agriculture self employ-ment, geographically concentrated in the centre and southern regions, the village stratum and rural areas, with a deepened social impact due to higher dependence on remittances, which are falling dur-ing the crisis period in rural areas. Regarding the type of economic activity, we observe a deeper impact for farmers and agricultural employees with a deeper impact coming from diverse losses (employment income + self employment income + remittances) for lower quintiles. Households with children are more vulnerable to the effects of crisis than households without children, with households with one child the most vulnerable. Differences regarding area, stratum or employment status of household head show the same trends as for all households. Regarding households with children run by women, they have a lower income from employment, self-employment in agriculture and non-agriculture, with the family budget being compensated by higher income from remittances. These households will be more vulnerable than those run by men, if the remittances keep falling.

6.2.3. Employment income remittances and changes in income distribution

Figure 2: Structural changes in income sources between October 2008 and March 2009 (national level)


Although official statistics in the agricultural sector reflect an increase in output of 3.7 per cent in the first quarter of 2009, independent national research on the impact of the crisis suggests that agri-culture is affected by the economic crisis just as much as other sectors of national economy. During 2008 and 2009, many rural areas showed visible symptoms of crisis, which were even recognized by official institutions. In 2008, a spectacular collapse in the prices for the main agricultural products

24 See: Prywes and Cnobloch, Moldova: child poverty and parental presence, evidence from HBS of 2007, 2009.


35

40

45

50

Oct Nov Dec Jan Feb March

beforeduring

Income from self-employment in non agriculture


6810121416

Other income, including remitances

0

5

10

15

Oct Nov Dec Jan Feb MarchOct Nov Dec Jan Feb March

beforeduring

2022242628

beforeduring

beforeduring

Oct Nov Dec Jan Feb March

31

took place: cereals and legumes, fruits and grapes, milk. This price collapse was followed by a sig-nificant increase in stocks. According to the NBS, in the period January-March 2009, sale prices fell sharply on crops, by about 60.1 per cent compared to same period of the previous year.25

Household income from agricultural activity depends on the feasibility of production and market prices. Data indicate that market prices for the wholesale and retail trades26 show a significant de-crease from previous periods.

A fall in manufacturing production volumes also has a negative effect, which limits the scope for mar-keting agricultural produce, especially fruits and vegetables. The period January-March 2009 was marked by a reduction in manufacturing production volumes, which accounted for only 71.3 per cent (in comparable prices) of the amount obtained in the same period of 2008 (the production volume for the processing and preserving of fruit and vegetables constituted 72.2 per cent of the total) .27

Box 1: Production decrease

According to the Ministry of Economy and Trade,28 comparing the January-March 2009 period with the same one in 2008, the production volume of the food and beverage industry fell by 21.5 per cent. This sector saw its share in the overall production index fall by 7.1 per cent. Falls were observed in almost all activities.

It is important to keep in mind that the fall in mean agricultural income only takes into account changes for households who are still receiving agricultural income during the crisis. But it does not reflect the income loss in real terms for all people and households who lost their employment or opportunities for income generation in agriculture. Out of some 88,000 people employed in agri-cultural activities29, more than 5,000 farms (from a total of 386,208 farms nationwide in 2008 to 380,935 in 2009) have abandoned agricultural activity since the last harvest period30.

Income from agricultural activity continues to be the main income source for the rural population. It is the main income source for 58.6 per cent of the total population31. For an agricultural worker, the average monthly wage is much smaller than the national mean wage. Thus, according to NBS data, in the first quarter of 2009, the average monthly salary of an employee in agriculture was only 48 per cent of the average monthly wage at the national level and reached only 87 per cent of the subsistence minimum for the working-age population32.

Of course, every year there is a fall in disposable per capita income between autumn and winter. At a national mean this fall was 5.9 per cent between 2007 and 2008 but 7.2 per cent between 2008 and 2009. So, in relative terms the usual income decrease which occurs every year towards winter time, due to the lack of agricultural activity, on a national average basis, during the crisis period is 21 per cent greater than in the same period before the crisis.33

25 Aplostol, P., Republica Moldova între vis şi realitate. 2009, http://petruapostol.wordpress.com/2009/08/01/republica-moldova-intre-vis-si-realitate/ 26 http://www.statistica.md/public/files/serii_de_timp/preturi/preturi_agricultura/1_ind_trim_pret_agric_2000_2009.xls 27 BNS. Activitatea industriei în Republica Moldova ianuarie-martie 2009. http://www.statistica.md/newsview.php?l=ro&id=2585&idc=168 28 http://www.mec.gov.md/files/documents/Evolutia%204%20luni%202009.pdf29 BNS. Forţa de muncă în Republica Moldova: ocuparea şi şi şomajul în trimestrul I 2009.30 Chivriga, V., Evoluţia pieţii funciare agricole în Republica Moldova. IDIS - Politici Publice nr. 4 2009, pag. 14. http://www.viitorul.org/public/2164/ro/Evolutia%20Pietei%20Funciare.pdf 31 BNS. Structura populaţiei stabile, pe sexe şi vârste, la începutul anului 2009. http://www.statistica.md/newsview.php?l=ro&idc=168&id=260232 Own calculations based on NBS data. http://www.statistica.md/public/files/publicatii_electronice/Raport_trimestri-al/Raport_I_2009_ROM.pdf; http://www.statistica.md/public/files/publicatii_electronice/Raport_trimestrial/raport_II_2009_rom.pdf 33 Between the 4th quarter of 2007 and the 1st quarter of 2008 disposable per capita income decreased 5.9%. Between the 4th quarter of 2008 and the 1st quarter of 2009 disposable per capita income decreased 7.2%, which is equal to a relative difference of 24.1 per cent between the two numbers, with all prices expressed in terms of prices in October of 2007.

32

Disaggregating income losses by urban and rural areas (the latter make up 63 per cent of all Moldovan households), urban disposable income appears to have continued increasing slightly during the cri-sis period. On the other hand, rural income fell by more than 7 per cent, driven by a fall in employ-ment income (12 per cent) and agricultural self employment income (25 per cent), which together account for 47% of all rural income. Interestingly, even comparing periods of six months’ duration, where the crisis only start to impact in the second half of the period observed, starting from Janu-ary 2009, we can already see an important difference in remittance receipts, which still increase in urban areas but are already stagnant (see figures 2 and 4 below) in rural ones.

The stagnation in remittance volumes in rural areas (according to household survey data) suggests that migrant population also is suffering as from salary cuts abroad as a consequence of the crisis. If so, less money may be available to be sent home. As a matter of fact, IOM (2009) reports that 9.7 per cent of those who definitely returned to Moldova did so because of a fall in salary in their host countries.

Employment activity is an important income source for the rural population but its contribution of 26.5 per cent of household income is less than half of its contribution to urban households. Revenue from individual agricultural activity represented 20.5 per cent of disposable income. Note that most income from farming is the value of goods for self consumption. Some 93.1 per cent of all cash income from rural households comes from selling agricultural production. Nevertheless this cash income represents only 6.9 per cent of all revenues in rural households34, which means that the rural household economy relies widely on self-consumption but that there are some goods and services only cash income can provide access to. If cash income falls, access to these services will be seriously restricted.

Both cities and towns still show rising incomes, driven by a general wage increase in urban areas which lasted until the end of 2008.35 Cities and towns also show a fall in agricultural income, but since the amount of agricultural income is insignificant in urban areas, the overall effect of a mean increase in per capita income remains.

Figure 3: Money Transfers from Abroad by Individuals (natural persons; residents and non-residents) via banks in the Republic of Moldova (in millions of US$)

Source: National Bank of Moldova

34 BNS. Veniturile şi cheltuielile populaţiei în trimestrul I 2009. http://www.statistica.md/newsview.php?l=ro&idc=168&id=2667 35 See Expert Grup 2009, Impact of the global financial crisis on local communities in Moldova. The number of employ-ees who worked effectively decreased by 5 per cent in Q1’09 compared to Q1’08. During the same period the wage bill increased only by 4 per cent, which represents a relatively high growth in remaining wages.

33

According to the National Bank data, official transfers from abroad fell significantly. After a steady increase since 2000, in the last quarter of 2008, according to balance of payments, remittances fell by 16.5 per cent over the previous quarter and had grown before by only 3.8 per cent over the same period in 2007.

Trends have not changed in 2009. During the first three months of the year, overseas transfers through the banking system fell by 43 per cent, compared to the same period the previous year36. Thus, house-holds dependent on remittances are subject to a high risk of falling revenues this year.37 According to IOM data from early 2009, 29.3 per cent of households in Moldova receive remittances. For 44 per cent of these, the amount of received remittances represents at least half or more of their household income.38

Return migrants is a major issue that faces Moldova in 2009 due to the lack of a sufficient supply of jobs, principally in rural areas, where migrants come from and are expected to return to. NBS data show that in 2008 the number of migrants (309,700) was 7.7 per cent lower than in 2007. Again, the number persons living abroad in the first quarter of 2009 was 18 per cent lower than expected.39

Box 2: Remittances and migration

For the first months of 2009 this year, according to an IOM (International Organization for Migration) survey on the impact of the crisis on remittances and households in Moldova (carried out in March 2009 among 2000 respondent households), the number of recipient households had dropped, as 20 per cent declared that they no longer receive any money. An additional 45.5 per cent admitted they receive “less” or “much less” money from abroad.40

More recently, cash transfers from abroad by individuals (residents and non-residents) via banks in Moldova fell in July 2009 by almost half (down 46.5 per cent) to 98.58m dollars from 180.8m USD in the same period of last year, according to National Bank of Moldova. This is the most substantial decline this year, a trend many economists did not anticipate, who after six months found that the rate of decrease in cash transfers from abroad by individuals has slowed in the second quarter and that this trend could continue in the next period.

Remittances in April fell by 42.6 per cent compared with the same month in 2008, while in May and June, the fall was 31.6 per cent and 25.6 per cent respectively. Economists were expecting that the trends of past years would continue, with remittances in July being higher than the previous month (as in 2008). However, this expectation proved to be wrong.

Business Expert magazine41 wrote that “the signs of revival of the economies of countries where Moldovans work are too weak to hope for an increase in their income and, respectively, in their transfers”. World Bank experts previously estimated that the growth rate of remittances from Moldovans abroad will be reduced in the years 2009-2010, but these flows will continue to be high even if the countries from which the transfers are made should experience a greater slowdown compared with the US and Europe (e.g. Russian Federation). The Ministry of Economy and Trade forecasted that remittances may decrease in 2009 by 300-350m dollars. Meanwhile the International Monetary Fund (IMF) has revised the forecast on inflows of transfers from individuals from 2.4 billion dollars to 1.1 billion dollars.

The volume of remittances has thus dropped by approximately 35 per cent during the first seven months of 2009. In the light of this, household budget survey data for the period January to March 2009 period are surprising since they show that remittances are still increasing in urban areas or stagnating at the previous level in rural areas, considering mean values.

36 Based on NBM data. 37 For more details on remittances see Box 2 below.38 IOM (2009). The Socio-Economic Impact of the Economic Crisis on Migration and Remittances in the Republic of Moldova.39 Expert Grup, 2009, page 27.40 http://iom.md/materials/studies_reports/2009_06_02_socio_economic_impact_eng.pdf41 http://www.businessexpert.md/index.php?r=12&s=1907&l=10

34

As a matter of fact, analysing the total level of remittances throughout the four household surveys which are the basis for the analysis in this report, an increase in remittances until December of 2008 is observed and a decrease of 6 per cent over the first quarter of 2009. All numbers are controlled for Consumer Price Index and are expressed in MDL as of October 2007. So, since throughout the social impact analysis in the whole report the “during crisis period” includes the months between November 2008 and March 2009, the mean values of received remittances by household include increases in remittances in late 2008 and decreases (rural area) in remittances in early 2009. The increase in remittances in urban areas, regarding reception of remittances considering absolute values, throughout the first quarter of 2009 is influenced by an appreciation of the exchange rate with the US dollar of some 10 per cent (since we express the value of remittances in MDL of October of 2007). But, even controlling for exchange rate appreciation, the absolute value of urban remittances in the first quarter of 2009 would be higher than the absolute value of remittances in the last quarter of 2008.

Figure 4: Total level of remittances

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data.Obs: In 2007 Oct prices

Therefore, according to HBS data, remittances to urban areas, even expressed in constant prices, were still increasing (19 per cent up) while remittances to rural areas were already decreasing in early 2009 (17 per cent down). Since the total amount of remittances going to rural areas is higher than the amount going to urban areas, at the national level we have a fall of 6 per cent for the total amount of remittances between January and March 2009 compared to the period between November and December 2008.

35

Already in late 2008 there were signs of increased migration of skilled urban-dwellers and an exhaustion of the pool of migrants from rural areas.42 Lower-skilled labour migrants tended in the past to be temporary and predominantly seeking work in the Russian Federation (e.g. loss of a job results in not leaving for a new cycle of migration) while skilled migrants tended to migrate to the EU and more often had plans to establish themselves permanently in the destination countries (ibid. p.18). Thus, it is probable that more migrants from urban areas, who are more highly skilled on average than Moldovans from rural areas, were able to secure permanent work and/or legal status in EU countries and account for a lesser drop in remittances to urban areas.

Income from remittances represents around 25 per cent of rural households’ total income, and their decrease may have a major negative impact on rural areas. An ex-ante analysis of the effects of remittances on income shows that about 11 per cent of households would fall from quintile one to quintile two if they received no remittances. In the same, way 9 per cent of quintile three, 7 per cent of quintile four and 9 per cent of quintile five would fall back to income levels corresponding to the poorest quintile (all these numbers from the first column of Table 10) if they stopped receiving remittances. The same effect can be observed throughout the other quintiles. Even the wealthiest quintile is dependent on remittances for staying in their income quintile. For instance, only 69 per cent of households belonging to the wealthiest quintile would remain in the same category even without reception of remittances. So, even with the shrinking remittance volumes, 36 per cent of households that actually receive remittances and are ranked in income quintiles higher than quintile one would fall back to quintile one without remittances. A further 19 per cent would fall back from higher quintiles to quintile two without remittances. Since employment income decreases signifi-cantly during the crisis period, households who receive remittances do now depend more heavily on these, even if the overall amounts of remittances are decreasing.

Table 11: Distribution of households by per capita income quintiles with and without remittances

Distribution of households by per capita income quintile excluding remittances

Q I (poorest) Q II Q III Q IV Q V (richest) Total

Col %Row

%Col %

Row %

Col %Row

%Col %

Row %

Col %Row

%Col %

Row %

Distribution of households by per capita income quintile including remittances

Q 1 64.2 100.0 20 100

Q 2 11.3 17.6 81.2 82.2 20 100

Q 3 8.5 13.3 8.2 8.3 86.7 78.4 20 100

Q 4 7.0 10.9 5.7 5.7 9.0 8.1 89.8 75.3 20 100

Q 5 9.0 14.1 4.9 5.0 4.2 3.8 10.2 8.5 100.0 68.6 20 100

Total 100 31.2 100 20.3 100 18.1 100 16.8 100 13.7 100 100


Table 11 shows how in response to the income changes mentioned above, the distribution of income sources is shifting. Within the crisis period, urban households depend more heavily on employment income and remittances, while rural households depend more heavily on income from self-employ-ment in agriculture, social payments and remittances. In the case of rural households two of these three main income sources are decreasing (income from agriculture and remittances) and increases in social payments alone cannot counterbalance these losses.

42 Labour Migration and Remittances in Moldova: Is the Boom Over?”, p.41 http://iom.md/materials/studies_reports/2009_05_05_remmit_boom_over_eng.pdf

36

Table 12: Changes in income distribution of mean per capita income by area

Urban Rural

Before crisis During crisis Before crisis During crisis

Disposable income per capita 100.0 100.0 100.0 100.0

Income from employment 55.2 57.6 27.9 26.5

Income from self-employment in agriculture 1.2 1.1 25.5 20.5

Income from self-employment in non-agriculture 7.9 7.9 5.0 6.4

Income from property 0.6 0.5 0.1 0.2

Social payments 12.8 12.1 16.1 18.2

pensions 10.7 10.1 13.6 15.5

child allowances 0.4 0.6 0.4 0.4

nominative compensations 0.5 0.4 0.6 1.0

Other incomes 22.3 20.7 25.4 28.2

remittances 12.3 15.4 22.1 24.6

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data; Obs: In 2007 Oct prices.

Employment income falls when wages decrease or when the available opportunities for work be-come more scarce. According to World Bank forecasts, the unemployment rate will exceed 10 per cent in 200943 (the unemployment rate in 2008 was 4 per cent44). In the first quarter of 2009 alone, unemployment rose to 7.7 per cent while the employment rate fell by 5.7 percentage points45 and the number of registered unemployed people increased by 35 thousand46. This can be explained by an increased registration rate at the National Employment Agency (NEA) in response to payment of cash benefits in case of unemployment. An additional reason for this increase in the number of registered unemployed persons is that many returning migrants do not find a job. Thus, according to the Labour Force Survey (LFS), in the first quarter of 2009, the ratio of persons working or seeking work outside the country was 12.7 per cent lower than in 2008. An increasing number of unem-ployed persons may have serious implications for state revenues and the budget and could require a review of passive labour market policies.

The reduction in budget revenues due to lower consumption and reduced levels of economic activity in the country puts payment of pension benefits and wages in the budgetary sector at risk. The IMF has already suggested cutting expenditures on budget sector salaries so that they do not exceed 10 per cent of GDP47.

43 WB (2009). Global Crisis Pushing Almost 35 Million People Back into Poverty and Vulnerability in Europe and Central Asia. http://www.worldbank.org.md/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/MOLDOVAEXTN/0,,contentMDK:22165459~menuPK:50003484~pagePK:2865066~piPK:2865079~theSitePK:302251,00.html 44 NBS. Situaţia social-economică a Republicii Moldova în ianuarie – martie 2009. 45 NBS. Forţa de muncă în Republica Moldova: ocuparea şi şomajul în trimestrul I 2009. http://www.statistica.md/newsview.php?l=ro&idc=168&id=260746 ANOFM. Numărul persoanelor aflate în cautarea unui loc de muncă. Raport statistic aprilie 2009. http://www.anofm.md/Sites/anofm_md/Uploads/raport%20statistic%2011%20mai.F201003B4B1F4AAE8A24F39ED3142E0E.doc 47 ADEPT &EXPERT-GRUP (2009). Implementarea reformelor iniţiate conform Planului de acţiuni RM-UE, evaluarea pro-gresului în perioada aprilie–iunie 2009. Dezvoltare şi reforme economice. EUROMONITOR nr. 6(15) Ediţia V. p. 22, http://www.expert-grup.org/library_upld/d210.pdf

37

Figure 5: Distribution of employment by economic activities

Source: NBS48

In the crisis period we observe an increase in the population employed in the services sector (whole-sale and retail, hotels, restaurants, etc.). Also, a significant fall in employees in agriculture has been noted, compared to the period before the crisis (Q1 2007/Q4 2008).

The HBS data show that during the crisis the number of people from households with children who work at least one hour per week fell. The number of persons from households with children who have been actively searching for a job for in the last 4 weeks increased (from 8.5 per cent to 9.3 per cent). We notice also the trend towards a lower number of working hours per week for all catego-ries of respondents. Respectively, 24.4 per cent of the representatives of households with children wish to work more hours per day during the crisis, compared to 22.7 per cent before. The number of children aged between 15 and 17 years who work at least one hour daily has increased by one percentage point since the crisis began.

The number of the persons from households with children, who are registered with the employ-ment offices increased insignificantly during the period under examination (from 0.6 per cent to 0.8 per cent).

48 http://www.statistica.md/public/files/serii_de_timp/forta_de_munca/populatie_ocupata/infraanuale/Distr_pop_ocup_activ_sexe_trim.xls

38

Table 13: Access to the labour market by periods (%)

Before crisis In crisisHH with children all HH HH with children all HH

Work (at least 1 hour per week) 59.3% 55.8% 58.9% 56.9%from them children 15-17 years

5.5%(29 children from 525) - 6.6%

(32 children from 484) -

Actively look for a job in last 4 weeks 8.5% 6.7% 9.3% 7.9%from them children 15-17 years

4.0% (21children) - 3.1%

(15 children) -

From people that have a job, want to work more 22.7% 19.0% 24.4% 20.7%

from them children 15-17 years

40.0%(12 children from 30) - 40.6%

(13 children from 32) -

At evidence at NAE 0.6% 0.3% 0.8% 0.6%Average of work hours during one week 33.2 hours 33.0 hours 32.6 hours 32.3 hours

children 15-17 years (indicated that WORK) 25.2 hours - 19.8 hours -

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data.Obs: In 2007 Oct prices.

Sixteen per cent of members of households with children thought it probable they would lose their jobs during the next six months. Those who still have these fears are from towns (25.3 per cent), retired persons (21.2 per cent) and entrepreneurs (56.5 per cent).

The HBS data investigate assessment systems and mediation support services for the unemployed. It shows that 44.9 per cent of respondents from families with children do not trust this system (42.2 per cent know nothing about it and could not assess it). Over 50 per cent of village inhabitants know nothing about this system.

Summary of access to employment and remittances

The usual income decrease which occurs every year towards winter time, due to the lack of agricul-tural activity is 21 per cent higher during the crisis period than in the same period before the crisis. Rural income fell by more than 7 per cent, driven by a fall in employment income (12%) and in agri-cultural self employment (25%), which together account for 47 per cent of all rural income.

During the crisis period, urban households depend more heavily on employment income and remit-tances, while rural households depend more heavily on income from self employment in agriculture, social payments and remittances. In the case of rural households, two of these three main income sources is falling (income from agriculture and remittances) and rises in social payments alone can-not compensate these losses.

Household survey data confirm that remittances to urban areas, even expressed in constant prices, were still rising (up by 19 per cent) while remittances to rural areas were already decreasing in early 2009 (down by 17%). Since the total amount of remittances going to rural areas is higher than the amount going to urban areas, at national level we have a decrease of 6 per cent in the total amount of remittances between January and March 2009 compared to the period between November to December of 2008.

Income from remittances represents around 25 per cent of the total income of rural households, and their fall may have a major negative impact on rural areas. So, even with the shrinking volumes remittances received, 36 per cent of households who receive actually remittances and are ranked in income quintiles higher than quintile one would fall back to quintile one without remittances. Another 19 per cent would fall back from higher quintiles to quintile two without remittances. Since

39

employment income decreases substantially during the crisis period, households that receive remit-tances depend more heavily on these, even if the overall volumes of remittances are decreasing.

According to the Labour Force Survey (LFS), in the first quarter of 2009, the ratio of persons working or seeking work outside the country was 12.7 per cent lower than in 2008.

6.3. Expenditure effects

6.3.1. Expenditure effects by area and stratas – all households

Consumption expenditure of households reported a significant decrease in real terms of 6.2 per cent at a national level. Households reduced spending on all goods and services with the exceptions of education, dwelling maintenance and communication expenditure. Socially, the most important decrease we observed was a fall of 13 per cent in food expenditures, a quite significant reduction considering that food expenditure accounts for about 40 per cent of the total household budget at the national level. Falling food expenditure could lead to consumption of food of lower quality and in lower quantities, which in the long run could be a threat to the health situation of the affected people or population groups. But since health indicators do not change as quickly as income or ex-penditure, for example, there is still no empirical evidence that the health situation is worsening. Nevertheless, the population and the authorities should pay attention to this phenomenon.

Table 14: Distribution of average per capita expenditures by strata (%)

National Urban Rural / Villages Cities Towns

Income change


Income change


Income change


Income change


Income change


Consumption expenditure per capita -6.2 100 -3.1 100.0 -8.9 100 4.7 100 -8.4 100

Food -13.5 40.2 4.6 37.9 -2.0 43.5 -5.9 35.0 -14.1 41.6Beverages -27.0 1.9 0.3 1.4 -14.0 2.4 -7.2 1.3 -28.0 1.4Clothes and foot wear -4.6 12.8 3.7 12.5 -10.6 13.3 10.3 12.5 -12.9 11.9Dwelling maintenance 8.8 15.4 -4.4 15.2 -23.7 15.7 31.3 14.4 8.1 16.7Dwelling equipment -11.2 4.2 -38.6 3.6 -41.9 4.4 -6.0 3.9 4.2 4.2Health 11.2 6.8 1.3 6.7 20.5 7.1 22.0 7.0 -8.4 5.8Transport -12.6 4.5 -25.0 4.7 7.8 4.0 -11.8 5.7 -15.6 3.7Communication 8.2 5.3 -5.5 5.7 -7.1 4.8 14.5 5.7 5.0 5.9Entertainment -13.5 1.8 -8.2 2.6 -32.6 0.9 -3.7 2.9 8.0 1.9Education 6105.2 0.7 -31.9 1.0 114.4 0.2 165.8 1.4 59.3 0.6Hotels, restaurants -13.7 2.3 -2.1 3.6 -32.5 0.7 -6.2 4.6 -23.5 1.7Others 2.4 4.2 4.2 5.3 0.0 3.0 16.8 5.6 -8.0 4.5


Resources have been reallocated from food consumption and towards dwelling maintenance, health and communications, which can be explained by increases in prices for these types of services. In 2008, tariffs for gas, electricity and heating were increased twice. Costs and tariffs for natural gas consumption grew by 20.6 per cent and electricity and heating costs by 22 per cent and 36.4 per cent respectively49.

Increased costs can be observed as well for health services, particularly the cost for compulsory medical insurance policy, which rose by 39.3 per cent in 2009. As a result, people resort to medical

49 ANRE (2008). Raportul anual de activitate a ANRE în anul 2008. Activităţile de actualizare a tarifelor la gazele naturale, la energia electrică şi termică. pag. 8-10 http://www.anre.md/upl/file/Rapoarte/Raport%20anual%202008%20total.doc

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insurance policy services only when it becomes unavoidable, and prefer to use emergency services that are free. About 30 per cent of the population of quintile one (the poorest) and about 16.4 per cent of the population of quintile five (the richest) do not have any medical insurance. Out of the total group of uninsured, 48.8 per cent mention that they do not have insurance because it is too expensive, 16 per cent think that they would still have to pay for medical services despite the fact that they are insured. By employment status, the composition of non-insured is as follows: 19.2 per cent are unemployed, 21.9 per cent are employees, 48.9 per cent are self-employed in agriculture etc., 9.9 per cent are self-employed in non-agriculture activity. Out of all insured people, only 1.7 per cent are individuals who have purchased their own medical insurance policy.50

Thus, households are forced to redirect their expenditure to cover those strictly necessary items. Consumption expenditure distribution by income quintiles shows that decreased consumption ex-penditure in essential goods and services (food, beverages, footwear and clothing), is observable throughout all quintiles, from the poorest to the wealthiest. The fifth quintile additionally showed a significant reduction in spending on dwelling equipment, transportation, recreation, hotels and restaurants, which constitute about 18 per cent of total fifth quintile expenditure.

Table 15: Average per capita expenditures by income quintiles (%)

Q I (poorest) Q II Q III Q IV Q V (richest)

Ex-pendi-

ture change

(%)

Expend-iture struc-ture in crisis

period

Ex-pendi-

ture change

(%)


period

Ex-pendi-

ture change

(%)


period

Ex-pendi-

ture change

(%)


period

Ex-pendi-

ture change

(%)


period

Consumption expenditure per capita

-11.8 100.0 -6.5 100.0 -10.9 100.0 0.3 100.0 -4.3 100.0

Food -15.1 47.5 -14.0 45.0 -12.3 45.6 -9.9 40.1 -15.2 33.1Beverages -26.2 2.5 -19.1 2.1 -31.6 2.2 -25.5 1.6 -27.9 1.5Clothes and foot wear -10.6 12.7 -0.7 12.2 -12.9 11.9 4.3 12.8 -3.5 13.4Dwelling maintenance -3.6 16.0 -3.4 15.4 12.4 17.1 11.0 14.5 18.5 15.1Dwelling equipment -27.5 3.2 -3.9 3.9 -27.4 3.6 2.8 4.8 -8.4 4.5Health -12.3 5.9 15.9 7.3 2.0 6.3 26.1 6.5 15.5 7.5Transport -5.0 2.4 -6.4 2.9 -37.9 3.0 -3.8 5.0 -8.1 6.4Communication 2.8 4.5 3.5 4.5 8.6 4.7 17.4 5.3 8.3 6.1Entertainment 37.7 1.4 2.5 1.1 -42.1 1.2 -0.5 1.8 -14.8 2.5Education 333.2 0.7 84.1 0.4 -36.4 0.4 218.0 0.6 142.0 0.9Hotels, restaurants -58.0 0.3 -34.2 0.8 -40.7 0.7 -4.5 2.2 -5.5 4.4Others -14.9 2.8 27.4 4.3 -18.4 3.3 25.7 4.9 -3.7 4.5

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data.Obs: In 2007 Oct prices.

Village households have reduced expenditure in all categories except for health, communications and education. It can be explained by the compulsory nature of such expenditures on communica-tions and education, and by urgent need in cases of health problems.

As a matter of fact, regarding the impact of the crisis and coping strategies, villagers’ behaviour is almost identical to what was observed and explained earlier in the report regarding those from rural areas.

50 BNS (2009). Rezultatele studiului privind Sănătatea şi accesul populaţiei la serviciile de sănătate în Republica Moldova. Realizat la solicitarea MS cu suportul WB. p. 3-5

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Town-dwellers continue to spend on home maintenance and equipment; these costs represent over 20 per cent of total expenditure. Both villages and towns show significantly reduced spending on food, clothing and footwear, and on transportation.

Box 3: Energy costs

Table B3.1: Energy costs - Cumulative inflation October 2007 to March 2008 (%)

Electricity 15.2Water and sanitation 58.6Natural gas network 28.2Heating 75.9

Source: NBS

Table B3.1 shows how energy costs, mainly heating, included in Table 15 in the category dwelling maintenance, have risen. Table 14 shows how only wealthier households (quintile III, IV and V) increased their energy expenditure significantly while there was no increase observed among poorer households. Data from the household survey cannot provide clear evidence on whether this lack of increase in expenditure is the consequence of an effective and well-targeted policy of energy cost subsidies or if poorer households decide not to increase energy expenditure. If we had to face the latter case there would be a serious problem with energy prices in the coming winter of 2009/10. Nevertheless, since energy prices were cut in September 2009, basically being set back at the level of late 2007, the threat of people being unable to pay for heating during the winter time seems to be much lower at the moment.

In comparison to those in other types of settlement, households in cities spent more money on clothes and shoes, but cut spending on transport, recreation, hotels and restaurants, which are quite significant, representing a share of over 13 per cent. They have increased spending on dwelling maintenance (by 14.4 per cent), the result of those services increasing in cost (see Box 4 below).

The reduced spending on food consumption, independently of residence area, could partly be caused by consumption of food of lower quality at cheaper prices. In this respect, the HBS data show a de-crease in consumption of purchased bread while it registered an increase in consumption of wheat flour for baking bread at home. Also there was a fall in consumption of beef and pork while there was an increase in the consumption of chicken meat (due to more affordable prices). Households give preference in their expenditure to vegetable oil, instead of butter and margarine; cut their con-sumption of rice and pasta while increasing consumption of beans, peas, etc.

Table 16: Monthly food consumption per capita

Before crisis In crisisQuantity per capita Quantity per capita

Bread from wheat flour, kg 5.61 4.82Wheat flour, kg 1.72 1.89Cow meat, kg 0.89 0.79Pork meat, kg 0.87 0.84Chicken meat, kg 0.52 0.86Butter, kg 0.33 0.32Margarina, kg 0.41 0.39Vegetable oil, litru 0.83 0.98Rise, kg 1.06 0.87Pasta, kg 1.18 1.06Beans and pieces, kg 0.38 0.44

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data.

42

The consumption expenditures by geographic areas reflect the same trend as income. The central and southern regions are most vulnerable to the crisis, registering a significant reduction in house-hold consumption. Therefore, in all regions, a decrease in expenditure on food and beverages, cloth-ing and footwear, housing and recreation facilities has been observed.

The population of the central region is most affected, both in terms of revenues and in all categories of expenditures, except for communication and education, spending on which is increasing, but in-significantly. Chisinau is the least affected region by the crisis, due to its favourable position as the capital city. However, even in Chisinau we observe the trend that the population is attempting to cut costs wherever this is possible, except for compulsory payments. We find decreased expenditure on food and beverages, housing facilities, transportation, recreation, hotels and restaurants.

Box 4: Food expenditure

Table B4.1: Geographical distribution of food expenditure changes – before and in crisis (%)

National UrbanRural / Villages

Cities Towns North Center South Chisinau

Food -13,5 -9,4 -17,2 -5,9 -14,1 -14,2 -17,4 -17,1 -6,4

Food in kind -26,1 -42,4 -22,6 -57,5 -28,0 -14,2 -27,2 -26,0 -62,8

Food in cash -8,2 -5,3 -12,7 -0,7 -11,9 -14,2 -11,3 -11,4 0,1

Source: NBS; HBS IV 2008 and I 2009; self processed data. Obs: In 2007 Oct prices.

We observe a larger fall in spending on food in kind consumption than for cash expenditure. In fact, food expenditure started to decline in early 2008 and had not recovered by March 2009, when our observation period ended. Since the fall in food expenditure began, the lowest point of in kind consumption was observed in October 2008 (only 60 per cent of the level in October 2007). The total fall in food consumption is higher in rural areas (almost double the urban level), while urban in kind consumption has fallen twice as much as rural consumption. We also observe a slight trend towards an increased importance for cash food consumption in both areas (urban and rural). As for the other issues analysed in this report, the highest negative impacts are observed in the central and southern regions. In kind food consumption and cash food consumption falls constantly throughout all household sizes. Nevertheless, we find a higher overall impact in households with two children and in households without children, which are more deeply affected by falls in cash expenditure.

6.3.2. Expenditure effects by area and stratum – households with children

After the outbreak of the economic crisis, the volume of monthly per capita consumer expenditures decreased both in households with children and in those without children. The households with chil-dren made the most significant cuts in food consumption, but also in the consumption of alcoholic beverages and tobacco, dwelling equipment, transportation, free time, cafes, etc., both in absolute and relative terms.

The precarious economic situation of families with children during the crisis period is revealed by the fact that most of them cannot make any savings. Only households with 2 children, those run by entrepreneurs, employees in the non-agriculture, or those whose heads have higher education manage to make some savings, the others use the savings made.

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Table 17: Expenditure change for households with children, couples and single parentes – befor and in crisis (%)

Couples with children up to 18 Single parent with children up to 18Expenditure

changeExpenditure

structure in crisisExpenditure

changeExpenditure

structure in crisisConsumption expenditure per person

-4.1 100.0 -7.4 100.0

Food -10.9 39.9 -15.2 40.6Beverages -22.7 1.8 -32.4 1.9Clothes and foot wear 0.0 14.4 -11.0 10.7Dwelling maintenance 8.2 13.7 12.7 17.6Dwelling equipment -12.8 4.3 -9.3 4.1Health 22.7 5.6 7.4 8.4Transport -11.3 4.9 -14.0 4.0Communication 3.8 5.7 16.0 4.8Entertainment -18.2 1.9 -9.1 1.7Education 60.0 0.9 150.0 0.4Hotels, restaurants -8.3 2.3 -18.2 2.3Other 9.8 4.7 -6.5 3.5

Obs: In 2007 Oct prices; Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data.

The analysis of the food products consumed proves that the consumption of pork, beef, potatoes, rice, pasta, any type of grits, including bread, fell in households with children. At the same time, the consumption of poultry, which is cheaper, flour, eggs and oil increased.

Expenditures on clothing and footwear in households with children increased during the crisis both for women (from MDL 153 to MDL 185), men (from MDL 138 to MDL 142) and children (from MDL 100 to MDL 111). Obviously, the families with children from urban areas allocate more money for clothing and footwear compared to those from rural areas.

Table 18: Quantity of per capita food consumption, selected products

HH with childrenup to 18

HH without childrenup to 18

Before crisis In crisis Before crisis In crisisWheat bread, kg 4.8 4.2 6.8 5.8Pasta, kg 1.0 0.9 1.6 1.4Beef, kg 0.7 0.7 1.2 1.0Pork, kg 0.7 0.7 1.1 1.1Poultry, kg 0.5 0.8 0.6 1.0Fresh fish, kg 1.1 1.1 1.9 1.8Milk, litres 1.4 1.3 2.1 2.1Cottage cheese, kg 0.4 0.4 0.7 0.7Eggs, pcs 5.5 5.8 7.9 7.6Butter, kg 0.3 0.3 0.4 0.4Oil, liters 0.7 0.8 1.0 1.3Potatoes, kg 5.0 5.2 8.2 6.9Rice, kg 0.9 0.7 1.4 1.1Any types of grits, kg 1.0 0.9 1.6 1.5Wheat flour, kg 1.6 1.8 1.8 2.1


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The relative differences between the rich and poor households with children shows that all families spend less on nutrition, alcoholic beverages and tobacco, transport and much more on dwelling maintenance, healthcare and communications. We ascertain that families with children reduce their nutrition, but the impact of these changes on children cannot be known, as the data are collected at the level of households and the allocation of resources within the households is not known

Analysis of consumer expenditures by quintiles also indicates that the poorer a household with chil-dren is, the higher its share of expenditure on nutrition, dwelling maintenance, alcoholic beverages and tobacco, and the lower its share of expenditure on clothing and footwear, healthcare, education, cafés and free time. But, if we analyse the absolute data, we see that households with children from quintile five, compared with those from quintile one, spend three times more financial resources on nutrition, four times more on dwelling maintenance, five times more on clothing and footwear and 10 times more on dwelling equipment, etc. (during the crisis period).

Table 19: Expenditure change and composition for households with children by income consump-tion quintiles (%)


Expend-iture

change


Expend-iture

change


Expend-iture

change


Expend-iture

change


Expend-iture

change


Consumption expenditure per person

-9.1 100.0 -10.4 100.0 -4.1 100.0 -2.5 100.0 -6.0 100.0

Food -11.8 53.5 -11.1 49.5 -10.3 43.6 -12.3 38.3 -12.2 31.1Beverages -12.3 2.8 -24.2 2.2 -24.1 1.9 -35.0 1.4 -17.1 1.5Clothes and foot wear

3.6 13 -16.5 12.4 3.7 16 -1.2 15.4 -0.4 14.2

Dwelling maintenance

-1.4 15.3 0.5 14.8 4.2 13.8 16.5 13.5 5.6 12.8

Dwelling equipment

-2.4 2.9 -7.5 3.2 11.4 3.6 0.2 3.7 -31.5 5.9

Health 6.5 3.4 16.2 4.8 -1.9 4.5 14.9 5.3 31.3 7.4Transport -50.0 1.1 -33.4 2.6 -4.1 3.7 -9.4 5.2 -13.0 7.4Communication 6.0 4.9 7.5 5.4 -0.8 6.1 5.9 6.3 -4.3 5.5Entertainment -39.4 0.6 -40.3 0.8 -16.1 1.4 12.2 2.3 -29.5 2.7Education -69.7 0.1 -40.3 0.2 43.9 0.6 56.0 0.8 135.0 1.5Hotels, restau-rants

-74.0 0.2 -60.2 0.4 -20.1 1 5.3 2.7 -14.5 4

Other -27.9 2.3 -17.3 3.6 13.4 3.9 15.3 5.2 10.9 5.9

Obs: In 2007 Oct prices. Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data.

Expenditure distribution throughout quintiles for households with children reveals a dramatic situa-tion for households with three or more children. Thus, 43.7 per cent of families with three or more children during the crisis period belong to quintile one, compared to 24.3 per cent of families with two children and only 13.9 per cent of those with only one child.

Analysis of the consumer expenditures of households with children by residential area and by quin-tiles reveals that during the crisis period the proportion of rural families with children in poor quin-tiles rose from 28.4 per cent to 30.2 per cent and fell in the higher quintiles from 11.2 per cent to 8.7 per cent. By geographical region, the proportion of poor families in quintile one increased in the southern region of the country from 23.2 per cent to 31.1 per cent.

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Table 20: Quintile distribution of households with children by area – before and in crisis (%)

Consumption QuintilesPeriod I II III IV V

UrbanBefore crisis 11.0 18.5 18.6 25.3 26.7In crisis 9.8 12.3 19.1 28.0 30.9

RuralBefore crisis 28.4 24.2 19.9 16.4 11.2In crisis 30.2 27.5 19.9 13.8 8.7

TotalBefore crisis 21.2 21.8 19.4 20.1 17.7In crisis 21.4 21.0 19.5 20.0 18.3


The distribution of households with children by quintiles and by the socio-economic status of the household head during the period before and after the crisis reveals an increase of over 10 percent-age points in the share of the households run by farmers in the poorest quintiles.

There is also an increasing trend towards households with children falling into lower quintiles in the case of families whose head has primary and secondary education, those run by people above 65 years of age, and those where children are taken care of by grandparents or other people.

Figure 6: Distribution of some categories of households with children in quintile one by periods, if compared with total means (%)


The crisis also caused some changes to the share of households with children in the higher quin-tiles. We can observe changes in the number of rural households with children in quintile five, those run by farmers, or those whose head has primary or secondary education, and those of entrepreneurs.

46

Figure 7: Distribution of some categories of households with children in quintile 5 by periods, if compared with total means (%)


Summary of expenditure effects

Households recorded a significant decrease in consumption expenditure in real terms at the national level of 6.2 per cent. Households reduced spending on all goods and services with the exception of education, dwelling maintenance and communication expenditure. The socially most important fall was a drop of 13 per cent in food expenditures, a quite significant reduction considering that food expenditure is about 40 per cent of the total household budget at the national level.

Households are forced to redirect their expenditure to cover those strictly necessary items. Resourc-es were reallocated from food consumption to dwelling maintenance, health and communications which can be explained by rises in prices for these types of services. Regarding the geographical distribution of distribution over population groups, changes in expenditure show the same trends as changes in income.

Expenditure distribution throughout quintiles, for households with children, reveals a dramatic situ-ation for the households with three or more children. Thus, 43.7 per cent of families with three or more children during the crisis period belong to quintile one, compared to 24.3 per cent of families with two children and only 13.9 per cent of those with only one child.

6.4. Compensation via Social Payments and access to social services

6.4.1. Social payments

Broadened social services and increased access to them can potentially reduce the impact of adverse situations and vulnerabilities. Table 19 shows how adjustments in social expenditure increased the amount of payments (by unit costs) but reduced the number of households that receive these pay-ments. In this way, beneficiaries are better off, but other households who used to receive these pay-ments (comparing the pre-crisis and crisis periods) might be negatively affected by the loss of social transfers as happens for all household sizes regarding payments of pensions and for households of four or and more persons with child allowances. Reductions of the share of child allowances in total

47

income can be explained either by many households being ineligible, or by presence the groups of families with children under guardianship or trusteeship, who do not receive the allowance because the parents of child are away for extended periods or working abroad51.

Table 21: Changes in average amounts and coverage of social payments by household size

Household size

Percentage change of amount of pensions

Percentage change of

households receiving pensions

Percentage change of amount of child allow-

ances

Percentage change of

households receiving child

allowances

Percentage change of amount of nominative

compensations

Percentage change of house-holds receiving

nominative com-pensations

1-2 persons 17.7 -14.2 -17.1 40.0 14.9 -5.63 persons 27.5 -3.4 16.8 7.0 11.8 -12.54 persons 13.4 -21.2 47.3 -43.1 20.6 -8.95 and more persons 19.2 -9.3 20.3 -7.8 20.7 18.5Total 19.0 -13.1 23.8 -15.2 16.2 -4.3


Social benefits have more weight in household income during the crisis period (18.2 per cent) than before (16.1 per cent). The growth in social payments can be explained by the indexation of pensions (17 per cent in 200852) and increasing amounts of nominal compensations.

The increase in social payments was influenced by nominal compensation growth, which doubled during 2008 because of the need to compensate for increased prices of education (utilities) and en-ergy (electricity and heating)53. Also, there was an increase in the compensations for heating during winter time (support for buying coal and wood), with a larger number of beneficiaries of such pay-ments coming from the rural area.

Note that the many of the beneficiaries of normative compensations are disabled persons whose house-holds, right from the start, have a relatively lower per capita income (because disabled persons mostly do not provide income to the household. Additionally, the size of the normative compensations is small (1 per cent in mean terms, based on all households including those that benefit from this compensation and those that do not) and cannot significantly influence the mean income of all households. Neverthe-less, for elderly or disabled persons normative compensations frequently represent the most important or even the only income source. Table 20 shows how, behind the fall in receipt of social payments (ex-pressed in mean values) we have a fall in the number of households which receive social payments54. This decrease is even more pronounced in rural than in urban areas. To date, it is unclear whether we can already consider this decrease a general trend which will continue in the future, since the imple-mentation of the new law of social protection could quickly reverse this only recent tendency.

Table 22: Households that receive social payments

Before crisis In crisis Before crisis In CrisisUrban Rural

Count Col % Count Col % Count Col % Count Col %Household without invalids 1.004 86.0 880 87.0 1.721 87.7 1.524 85.3Household with invalids 164 14.0 132 13.0 242 12.3 262 14.7Total 1.168 100.0 1.012 100.0 1.963 100.0 1.786 100.0


51 Punctul 3 alin. b din Hotărîrea Guvernului 581 din 25.05.200652 MSPFC (2009). Annual Social Report 2008 (draft). p. 19 53 Hotărîrea Guvernului nr. 141 din 13.02.2008 şi Hotărîrea Guvernului 941 din 5.08.2008. http://lex.justice.md54 Urban beneficiaries decreased from 1,168 to 1,012 (-13%) and rural ones from 1,963 to 1,786 (-9%)

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The HBS data show that the crisis did not cause the fall in social payments (allowances for children, family compensation, pensions) provided to families with children. With respect to the social aid provided under Law no 133 as of 13 June 2008, some 3.2 per cent (16 people) of the representatives of households with children, interviewed as part of the HBS, applied for social help during December 2008 – February 2009. As the program had been in force since 1 October 2008 for families with at least one disabled member and since 1 January 2009 for families with one or more children, only in 0.8 per cent of cases (for 4 persons) had the decision on the provision of aid been taken by the date of interview. The average amount of social aid provided varies between MDL 176 and MDL 990. Nevertheless, these results, due to the low number of observations (between 4 and 16), are not statistically significant.

Most of those who applied for social aid are from rural area. Representatives of all quintiles submit-ted applications for social aid, but most of them were from the poorest quintiles. As far as the socio-economic status of the household head is concerned, most of those who applied for social aid were farmers, people involved in non-agricultural activities and retired persons.

It is significant that 47.2 per cent of the representatives of household with children did not know about the social aid. Another 36.1 per cent knew, but they mentioned that they did not meet the requirements. A further 7.3 per cent said they did not need it, 2.9 per cent dint not have the docu-ments needed to apply for social aid, and 3.2 per cent said that the procedure for aid provision were too complicated.

Figure 8: Knowledge of the Law on Social Aid among households with children, %

Source: NBS, Social exclusion survey 2009.

At the same time, the HBS data states that 28.4 per cent of the representatives of households with chil-dren trust the state pension system a little or not at all. Over 33.8 per cent of the persons from house-holds with children from cities, 34.5 per cent of representatives of households with three children and more and 36.2 per cent of entrepreneurs trust the state pension system a little or not at all.

The social assistance system is not trusted by the population. Namely, 33.6 per cent of representa-tives of households with children trust this system a little or not at all (while 27.1% know nothing about it). City inhabitants (42.5 per cent) and households with three or more children (39.9 per cent) trust it even less.

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6.4.2. Access to healthcare services

Children aged between 0 and 5 years of age are insured by the state and eligible for subsidised medi-cines under the Compulsory Health Insurance Programme and the Health Care Services Programme. Children aged up to 18, if they are not employed, benefit from the aforementioned programs, but they do not receive subsidised medicines.

At the same time, the HBS data reveal that 54.8 per cent of the representatives of households with children trust the health insurance system a little or not at all. The proportion of members of house-holds with children that do not have compulsory health insurance policies rose from 21.8 per cent before the crisis to 23.9 per cent during the crisis.

24.2 per cent of people aged between 18 and 57 (for women) and 62 years (for men) from house-holds with children did not have compulsory insurance policies in the first quarter of 2009, the dis-tribution by gender being equal. The absence of a compulsory health insurance policy is due to the following motivations: 9.4 per cent of these individuals think that the policy is too expensive; 6.1 per cent are not employed, but hope to become employed in the future and obtain a health insurance policy; 3.6 per cent think that it makes no sense to purchase the policy; a further 5.1 per cent work unofficially or abroad or think they do not need it.

In particular, 36.7 per cent of parents of children from the central region who do not have insurance policies, 30.9 per cent from the northern region, and only 19.3 per cent of those from the southern region and 13.1 per cent of those from the municipality of Chisinau . More than two thirds of parents who do not have a compulsory health insurance policy are from rural areas (72.6 per cent of the total number of uninsured), and these parents argue the policy is too expensive.

Those without compulsory health insurance usually have general and vocational education (67.2 per cent), and according to socio-economic status they are farmers (31.8 per cent) and employees in the non-agricultural sector (21.6 per cent).

Over 50 per cent of parents without insurance are from the poorest quintiles of the population (32.1 per cent from quintile one and 21.7 per cent from quintile two).

Analysis of expenditures on health care services within households with children shows that people in quintile five were spending about MDL 113 a month on health care before the crisis and MDL 150 during the crisis, compared to a person from quintile one who was spending 10 times less. There-fore, both before the crisis and during crisis, the access of children from poor families to health care services not covered by the compulsory health care insurance system is still quite limited.

Table 23: Average per capita monthly expenditures on health care services in households with children, by consumption quintiles and periods


Before crisis In crisis Before

crisis In crisis Before crisis In crisis Before

crisis In crisis Before crisis In crisis

MDL 13 14 26 30 41 40 56 65 113 150

% 2.9 3.4 3.7 4.8 4.4 4.5 4.5 5.3 5.3 7.4


The average per capita monthly expenditures for the health care services during the aforementioned periods increased both in the households with children and in those without children. Breaking these down expenditures according to the number of children in a family shows that in households with one child these expenditures rose from MDL 51 to MDL 73, while in households with two chil-dren expenditures did not change, staying at MDL 44. In households with three or more children, the increase amounts to only MDL 3 per capita. Households with three or more children spend on

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average half as much per capita for health care services than those with one child. At the same time, in households without children, per capita average monthly expenditures on health care services are over three times more than in households with three or more children, and over twice as much as in those with 2 children.

Table 24: Average per capita expenditures for health care services, MDL

HH with 1 child up to 18

HH with 2 children up to 18

HH with 3 and more children up to 18

Households without children up to 18

Before crisis 51 44 27 94In crisis 73 44 30 101


Households with children from rural areas spend less on health care: MDL 40 per capita before the crisis and MDL 42 during the crisis, compared to MDL 51 and MDL 72 for those from urban areas in the aforementioned periods.

6.4.3. Access to education

According to HBS data, enrolment in pre-primary education fell by 2 percentage points during the crisis period. Thus, 60 per cent out of pre-school age children benefit from preschool or school edu-cation. Three quarters of the children who do not go to preschool institutions are from rural areas and only a quarter are from urban areas. This situation is explained by the high cost and child care being provided by grandparents or parents if they are not employed, by the lack of pre-schools in some rural localities55, and the large distances children have to travel to reach them.

The number of children aged between 15 and 17 years who are not enrolled in pre-school institu-tions or schools is small – 11 persons (1.0 per cent) before the crisis and 4 people (0.4 per cent) dur-ing the crisis. The causes of non-enrolment are determined mostly by the degree of disability of the children and the presence of some chronic diseases.

Table 25: Enrolment of children in educational institutions

Before crisis In crisisNo of children in the sample

%No of children

in sample%

Children of 2-6 years 496 100 493 100Kindergarten, nurseries 301 60.7 289 58.7Primary school 8 1.6 11 2.2Do not attend any institution 187 37.7 193 39.1Children of 7-15 years 1,150 100 1,058 100Kindergarten, nurseries 11 1.0 9 0.9Primary school 419 36.4 391 37.0Lower secondary school 698 60.6 639 60.3Upper secondary school, college 11 1.0 15 1.4Do not attend any institution 11 1.0 4 0.4


Something that features in all households with children from the Moldova is that education expendi-ture makes up the smallest portion of total consumption. These expenditures account for less than 1 per cent of household total consumption. But there are significant differences in levels of expendi-

55 According to the data of NBS, in about 270 villages in Moldova there is no pre-school institution. Copii Moldovei. – Chişinău, 2008.

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ture on education depending on area of residence. Households with children from villages spend less than a tenth what those from the cities spend on education: on the average MDL 1 per capita before the crisis and MDL 2 during the crisis, compared to MDL 7 and MDL 8 for those from towns and MDL 12 and MDL 24 for those from cities. It is obvious that expenditures for education are not distributed equally during the year, being greater at the beginning of the school year. Nevertheless these indicators might not be able to capture the complete picture of what is happening with edu-cation expenditure, since we are dealing with mean per capita expenditure data for all households with children (weather enrolled or not). A more detailed way to analyse education expenditure trends would be to take a look at expenditure per student.

The larger the number of children in the family, the smaller the per capita educational expenditures after the crisis impact. Thus, in a family with one child below 18, the average per capita monthly expenditures on education amounted to MDL 5 before the crisis and MDL 12 during the crisis, com-pared to a family with two children, where the equivalent numbers are MDL 5 and 6 for the men-tioned periods, and for families with three children MDL 2 and 4 respectively. Therefore, families with three or more children spend a third as much per capita each month during the crisis on educa-tion than those with one child.

Entrepreneurs spend the most on their children’s’ education as a share of their total consumption (1.8 per cent of total consumption), as well as employees in the non-agricultural sector (0.8 per cent of total consumption).

Average expenditures for education from households with children from the lower quintiles, com-pared to the higher ones, also fell during the crisis period. So, the discrepancies between the expen-ditures of households for education increased: the richest quintile spends MDL 31 (1.5 per cent of the average per capita monthly expenditures) a month on education in the crisis, while the poorest quintile spends MDL 1 (0.1 per cent).

Table 26: Average per capita monthly expenditures on education in households with children, by consumption quintiles and periods


Before crisis

In crisisBefore crisis




In crisis

MDL 1 1 2 1 4 5 6 10 13 31

% 0.3 0.1 0.3 0.2 0.4 0.6 0.5 0.8 0.6 1.5


Summary of social payments and access to services

Increased amounts of social payments went hand in hand with a reduced number of covered house-holds, but even for households who still do receive social payments, the observed increase is not high enough to compensate for income losses caused by the crisis. HBS data do not allow an as-sessment of the impact of the most recent changes to social payments established by law Nr. 133. Potential benefits of this law might be limited due to the unsatisfactory dissemination of information regarding the law. Some 47 per cent of households interviewed did not know anything about this law, a worrying fact considering the mechanism of self selection applied for these programmes.

We observe an increase in the number of members of households with children that do not have com-pulsory health insurance policies from 21.8 per cent before the crisis to 23.9 per cent during the crisis; health care expenditure increased in the mean in all quintiles apart from quintile three (the middle class). Enrolment in pre-primary education fell by 2 percentage points during the crisis period.

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6.5. Subjective assessment of the standard of living

66.2 per cent of the representatives of households with children think that the household standard of living is satisfactory, 18.4 per cent think that it is low or very low, compared with 15.3 per cent who think that it is high or very high. When asked to compare the household standard of living with that of the previous year, 52.9 per cent said it was the same, 27.2 per cent said it is worse or much worse, and 20 per cent said it is better or much better.

In the opinion of 63.6 per cent of representatives of households with children, the financial status of their household compared to other households, is neither better nor worse than that of the majority, 24.3 per cent say that it was much worse or somewhat worse than that of the majority, and only 12.1 per cent said it was somewhat better or much better than that of the majority. Re-spondents from rural areas are more likely to see the financial status of their households as being worse or much worse (27.6 per cent), compared to those from urban areas (20 per cent). There are also significant differences related to this in the appraisal of the financial status in towns (26.9 per cent) and cities (14.8 per cent). Financial status was regarded as being worse by representa-tives of households with one child (26.6 per cent) and those with three or more children (25.3 per cent), compared to households with two children (20.9 per cent). 44.3 per cent of households managed by retired persons, 37.9 per cent of those employed in the agricultural sector, and 34.1 per cent of farmers say that from the financial point of view their status is worse or much worse than in other households.

One quarter of households provided aid in the form of money or food to other people during 2008. 30 per cent of members of households from urban areas, compared to 20 per cent of those from rural areas provided support. According to socio-economic status, the aid was provided by employees in the agricultural and non-agricultural areas and entrepreneurs. And according to the number of children in the family, the aid was provided most frequently by households with one or two children.

At the same time, 34.4 per cent of household heads said they had received aid from other people during the last year. Almost half of households from urban areas received aid (48.8 per cent, espe-cially those from cities) and only 23.2 per cent from rural area households. We found that families with one child were most likely to receive aid (38.5 per cent).

The HBS data reveal an increase during the crisis in the proportion of households with children that have difficulties paying for electricity (up from 18.4 per cent to 22.6 per cent), natural gas (up from 29.2 per cent to 31.5 per cent) and for central heating (up from 48.4 per cent to 59.3 per cent). In the case of families with many children, the share of those who have difficulties with paying for heating and electricity is greater. In particular, households with children whose head’s main income source is the unemployment allowance, social payments, or employment in the pri-vate sector, face difficulties paying for electricity and heating. It is obvious that the greatest share of those who encounter difficulties when paying for the aforementioned services is a part of the poorest quintile.

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Figure 9: Difficulties in payment for / procurement of the basic services


52.6 per cent of households with children cannot afford adequate heating of their homes. The ma-jority of them are households with children from the cities, households with one or three or more children, and households with children run by retired persons.

During the crisis period, there was an increase from 40.2 per cent to 42.7 per cent in the number of households with children who have difficulties buying food products. Thus, 56.9 per cent of households with children cannot eat meat or fish every second day or when desired. Most of them are from the ru-ral area, households with three or more children, or households run by retired persons and farmers.

With respect to the procurement of clothing, we would like to emphasize that 58.7 per cent of the households mentioned they could not afford to buy new clothes, only second-hand. These are the persons from rural areas and towns, households with three or more children, or households whose head is a farmer, retired or employed in the agricultural sector.

Figure 10: Subjective perception of household status, %


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7. PovertyA look at poverty levels previous to the crisis shows the trends described so far hit regions in Moldova where poverty levels are higher than the national mean. Additionally, 2007 (pre-crisis) poverty levels dropped after a significant period of growth which helped to reduce poverty. Traditionally, poverty levels in regions which are most affected by the crisis used to be higher. So the positive impact growth had on poverty reduction in recent years seems not to be sustainable. Figure 12 shows how the lowest-income groups (at the left end of the curve) benefited positively and with highest values of growth, in the period 2006-2007, while households belonging to the upper part of the income distribution (the right end of the curve below), did not benefit from growth. In other words, the low income groups which benefited most significantly from growth in the past are now the groups most affected by the crisis.

Figure 11: Incidence of real growth, 2006-2007

Source: MET, Poverty and Policy Impact Report 2007

When income decreases and official welfare measures are defined by income levels, it has to be expected that poverty rates will rise. The report has clearly shown that there are three main sources of income loss: agricultural income, employment income and falls in remittances. To date it is not clear which of these three channels will finally be responsible for what degree of aggregated welfare losses in Moldova. We should not necessarily think about these three channels regarding the scope of their impact but regarding their different characteristics and what they imply for a quick recovery and which of them should or could be motors for such a recovery. Apart from the scope of their im-pact, remittances should mainly be considered as a source of income loss whose recovery does not depend on national efforts or policies.

The World Bank realised a poverty increase simulation based on 2007 data, considering different scenarios of remittance performance.

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Box 5: Poverty Simulation World Bank

Table B5.1 Poverty Rates Under Alternative Scenarios of a Worker Remittance Shock (in percentage points shares of the Moldovan population)

Assumption on the average rate of consumption of remittances: actual 2007

Simulations under alternative assumptions20% 40% 60%

A. 2/3 of seasonal workers don’t go to Russia & Ukraine 25.9 28.3 29.0 29.3B. 15% of long-term migrants return home 25.9 26.3 26.4 26.5C. Combines scenarios A & B 25.9 28.9 29.5 30.0

Table B5.2 Changes in Poverty Rates Under Alternative Scenarios of a Worker Remittance Shock (In percentage points share of the Moldovan population)

Assumption on the average rate of consumption of remittances:

Simulations under alternative assumptions20% 40% 60%

A. 2/3 of seasonal workers don’t go to Russia & Ukraine 2.4 3.1 3.4B. 15% of long-term migrants return home 0.4 0.5 0.6C. Combines scenarios A & B 3.0 3.6 4.1

Figure B5.1: Estimated Proportion of Migrants from each quintile of consumption expenditures, 2007 (in percentage points)

In the simulation three scenarios were considered

Two-thirds of seasonal migrants do not leave for Russia and Ukraine;A. 15 percent of long-term migrants return home;B. Two-thirds of seasonal migrants to Russia and Ukraine do not leave and 15 percent of C. long-term migrants return home.

Results: The results from these scenarios are serious but not catastrophic. The layered shock (Scenario C) leads to a 3 percentage point increase in the poverty rate to 28.9 per cent assuming that households consume 20 per cent of their remittances (Tables 1 and 2). Past econometric works suggests that the 20 per cent figure is the best assumption for Moldova. But even if households consume 60 per cent of their remittances, the poverty rate in Scenario C rises by only 4.1 percentage points to 30 per cent.

Intuition: These shocks are not catastrophic because migrants come from the relatively rich part of the population judging by the distribution of consumer expenditure (Figure 1). This is true even when consumer expenditure is adjusted to remove estimated consumption paid for by remittances. In sum, relatively wealthy groups bear much of the burden of the shock.

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Understatement of poverty impact: However, these results probably understate the impact of the remittances shock because the simulation models only the direct impact on household consumption expenditures. This initial reduction in consumer spending would induce further cycles of reduced spending, although some of the contractionary impact would leak abroad through reduced imports. Moreover, lower remittances might induce depreciation of the Moldovan Lei and cut real consumption by raising domestic prices of natural gas and other imports.

Source: World Bank (2009).

The analysis shown in this report indicates that the World Bank’s poverty increase simulations seem to underestimate possible poverty effects. On the one hand side it is now clear that the loss of remit-tances does seriously affect poor households and it can be seen clearly that the effects of income losses from agriculture and employment in the country are important.

Even if there are still no official poverty data for 2009, for this report we tried to approach the ques-tion of what might happen below the poverty line during the crisis period, independently of what this poverty line is and where exactly 2009 first quarter poverty rates might stand. The question of what happens below the poverty line provides two kinds of additional responses to the basic ques-tion of “how many more poor people will there be in consequence of the crisis?” Additionally, re-garding those who are already below the poverty line, it is necessary to ask how much poorer they may get and how much inequality this will lead to.56

For our poverty estimates we are trying to take advantage of the following fact: official poverty rates for 2007 were estimated by NBS taking into account prices of October of 2007. All prices and costs taken into account for this study, until March of 2009 inclusive, are expressed in the prices of Octo-ber 2007, so they are perfectly comparable with the 2007 official poverty line, with one restriction. If there were changes in consumption patterns and in the weight a certain good would have within the consumption basket, we would not take these changes into account. So even if we are not dealing with a completely precise poverty line and even if we are using non-official data, our exercise below can show poverty trends regarding crisis impact.

For poverty estimates, from a methodological point of view, usually regional deflators and data from a 12-month period are used. None of these applies, so the data shown below might be an overesti-mate. Nevertheless, the internal dynamics of the data (Table 28) will still be true.

Table 27: Estimated poverty rates before and during the crisis

National Urban Rural

Before crisis



In crisis

Complete poverty status 24.0 31.2 14.0 16.4 31.2 41.9

Food poverty status 3.3 3.5 1.2 1.2 4.8 5.1

Poverty gap according Complete poverty line

5.7 7.8 3.1 3.8 7.6 10.7

Poverty gap according Food poverty line 0.6 0.6 0.2 0.4 0.9 0.8

Poverty severity according Complete poverty line

2.1 2.1 0.01 0,01 2.8 3.8

Poverty severity according Food poverty line

0.02 0.02 0.0 0.0 0.03 0.02


56 We are computing poverty gap and severity regarding a hypothetical poverty line.

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The official poverty rate in October 2007 was 25.9 per cent, the official poverty rate for 2008 was 26.4 per cent, which basically means that poverty did not decrease throughout 2008, even if there was sound economic growth. Table 27 above shows a poverty rate of 24.0 per cent. The difference between the rate of 24 per cent computed for this report and the 26.4 per cent reported by NBS for October 2008 relies on several facts. First of all, we use the 2007 poverty line and second, we take into account the mean levels of income for the whole period from October 2007 to March 2008, which are then compared to the mean values for the period from October 2008 to March 2009. So it is quite possible that poverty in early 2008 had decreased to levels close to 24 per cent but started to increase from the second semester of 2008. Of course there is a possibility that our poverty rates are underestimated, but the observed changes in poverty rates even in our exercise are quite con-sistent. Since the aim of this exercise is not to show exact poverty measures but dynamics in the change of poverty, we think this report provides interesting new empirical evidence that should be taken into account for policy design. Our estimates show that poverty might increase to 31 per cent at a national level, with the rural area the most affected. But, even more important for policy con-siderations is that the changes below the poverty line, making already poor people even poorer, are greater than the impact on persons falling below the poverty line.

Table 28: Relative changes below the poverty line

Poverty gap increase versus poverty increase – national

Poverty gap increase versus poverty increase – urban

Poverty gap increase versus poverty increase – rural

18.9% 25.9% 16.5%


Throughout a gradual poverty increase or decrease, outside a crisis scenario, empirical evidence shows that poverty rates and poverty gaps move in a more or less parallel way. When poverty falls by 3 per cent the gap does the same and vice versa. In our crisis scenario, nevertheless, the poverty gap increases much more quickly than poverty is expected to increase. The table above shows the percentage differences between the gap increase and the poverty increase. We can see clearly how the gap widens to almost 19 per cent more than the expected poverty increase (of whatever level) and that this increase is even greater in urban areas than in rural. These data are consistent with our analysis throughout this report showing that the bigger poverty effect will occur below the poverty line and that there is a considerable urban poverty increase, even if at first glance the rural area seems to be harder hit by the crisis.

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8. Community impactOf course, among the phenomena mentioned throughout the report, economic and social phenom-ena take place at a community level, precisely where people live and work. Higher unemployment, lower income, increased poverty, lower economic activity, less consumption, all these and other visible facets of the crisis can have a significant impact on quality of live at the social level and cause other additional problems like increased insecurity (crime and violence) or intra-family and psycho-logical problems (which are not the subject of this report).

Additionally, community encompasses the notion of local public administration. According to a re-cently launched UNDP report which analyses in detail the community impact of the crisis57, the on-going crisis has impacted local budgets as well. The existing system of local budget formation has kept local authorities in state of high dependence on state budget transfers. Despite the stated objective of fiscal decentralisation, the dependence of local budgets on central government has only grown in the last years. Many expenditure-related decisions that would save financial resources have to be coordinated with central government and this usually takes a long time (for instance, merging temporarily a school with a kindergarten). This has limited local authorities’ options for reacting to the crisis considerably, particularly in conditions of a protracted electoral campaign and a generalised political stalemate.

According to the same report, the impact of the crisis at the community level is stated that communi-ties are directly affected by the crisis through a decline in production and employment. The existing system of local budgeting has kept local authorities in a state of high dependence on central budget transfers. The collection of own revenues by local authorities has been largely good in the first half of the year, but it is expected to drop dramatically in the months to come due to the reduction in the number of employees and wages in the private sector. Since the reduction in transfers has not been approved officially, the government has been accumulating arrears on transfers to the local budgets. The reduction in transfers is not applied uniformly across local budgets. In the case of budgets with a higher share of local revenues, the impact is smaller. Also, budgets with positive balance at the beginning of the year have more resources to cope with the reduction in spending.

The local authorities’ response to the reduction in expenditures is limited to cutting some small expenditure on investment projects and some logistical expenses (travel, transport). To cope with the needed cut while preserving salary expenses, local authorities will need to reduce payments for communal services by 50–60 per cent. These include heating and electricity for public institutions, food for children in kindergartens and schools, etc. Since it is not possible to completely eliminate these payments, local authorities believe that they will start accumulating arrears on salaries as well by the end of this year.

Geographical targeting

Regarding community impact, we implemented several exercises to figure out which one, at least of the rural communities, would be most affected by the crisis, considering that rural areas are severely affected and considering that we have a SADI index available for these communities (at its 2007 level). We have not been able to compute a kind of “crisis or vulnerability index”, since we had no household panel data. The lack of panel data obliged us to look at structural patterns linking crisis vulnerabilities of groups and the spatial distribution of these groups on the basis of SADI index levels. Even if this methodology does not provide a crisis or vulnerability index, it gives some hints at higher or lower levels of vulnerability for different groups of households. The figures A1 to A7 in the annex show percentage changes in consumption compared to the SADI index and its constituent elements (income deprivation; demographic deprivation; economic deprivation; health deprivation; education deprivation and housing deprivation).

57 UNDP / Expert Grup 2009.

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We find a slight positive correlation between expenditure decreases and the overall SADI index, as well as for its income, economic and education components. For all four of them, a larger decrease in household consumption is correlated to a higher deprivation level. At the same time, for the de-mographic, health and housing components we observe a slightly negative trend which means that a greater decrease in household consumption levels is correlated to a lower level of deprivation in the considered components.

That a higher consumption decrease is positively correlated to a higher deprivation in income and economic performance is to be expected, but a correlation to the education component is not nec-essarily to be expected. This last result gives a hint that educational services (dependent on local budget execution) do perform worse in communities with a higher deprivation level. At the other hand in the more deprived communities, health services seem to perform better than education services. At the same time, communities with less demographic and housing deprivation tend to suffer greater falls in household consumption. This phenomenon could be linked to migration and remittance volumes (in the past) which were invested in housing.

So, in general, public policies which might attempt to offset negative crisis impacts should concen-trate on fostering economic and income generation activities and pay special attention to educa-tional services.

Nevertheless, as Figures A1 to A7 in the annex show, the described correlations are not very strong and do not apply in a uniform way to all communities. The task is to establish a rough system for geographical targeting (selection of most vulnerable and most affected communities) using informa-tion that is already available.

The analysis throughout this report has shown that rural areas suffer higher income and expenditure losses than urban ones and that central and southern regions are the most affected. Additionally we know that households with three or more than four persons are more vulnerable, as well as house-holds with children or households with only one child. For all these four groups we checked the avail-able data in the SADI database, regarding the distribution of these household groups throughout the communities in the central and southern regions and tried to identify most vulnerable rayons (which are not necessarily the most vulnerable ones according to the SADI index). The selection criteria ap-plied was that a given rayon had to be included at least in 3 out of our 4 different household catego-ries, within a subgroup of rayons below a given threshold.

For all four groups it seems that “poorer” communities (lower SADI index) are more severely hit then communities which are better off (poorer communities have a higher number of these especially vulnerable household groups). In all cases, the percentage of households living below the mean SADI index was higher than 50 per cent (55 per cent to 63 per cent and 72 per cent to 80 per cent for the 66 per cent threshold of SADI level – see tables A17 to A20 in the annex). Within these “poorer” communities, the ones which are the home of a larger number of vulnerable households can easily be identified. In our exercise, of four different groups of vulnerable households we have been able to prove that three out of the four communities which include a high number of vulnerable household groups are the same for each group (89 R-UL TELENESTI; 53 R-UL HINCESTI and 64 R-UL ORHEI in our case). Of course, such an exercise is far away from a perfect targeting but can give important first hints for urgent social protection measures or for selecting at least priority areas.

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9. Conclusions and RecommendationsConclusions

The report shows evidence regarding an already existing considerable social impact of the interna-tional crisis, in the first place increasing and deepening58 rural poverty. This trend could even get worse throughout 2009 and early 2010 if an economic recovery, which will start sooner or later, is not supported by strong social policies. Contrary to what has been expected for Moldova, the impact of the social crisis is not mainly driven by the loss of remittances. Our data show that employment and income loss is a much stronger crisis driver than expected, headed by a rural and agricultural de-pression. The report compares the six month period between Q4 2008 and Q1 2009 with the same period one year before. It finds a striking 25 percent contraction in income from self-employment in agriculture over this period, compared to a 2 percent decline nationwide (“self-employment in agri-culture” refers mainly to work of the many farmers who own small lots). Moreover, the report finds that food consumption in rural villages fell by 17 per cent, compared to a 6 per cent decline in cities. It also presents evidence of a shift away from high and mid-priced staples to cheaper foodstuffs. In this way, an income loss is translated into a reduced expenditure, but since possibilities for cash expenditure cuts are limited, in kind food consumption is the main coping strategy for rural house-holds, which in the long run might even have negative consequences for health conditions.

Further deterioration in poverty rates is also likely as the significant return of migrants continues, after doubling in pace in early 2009 compared to early 2008, with consequent falls in remittances and rising unemployment as those returning re-enter the domestic labour market. Given that mi-gration has been principally from rural areas, it is to be expected that the effects of large numbers of returning migrants would be felt more sharply there. Nevertheless, we can also expect urban areas to experience a significant negative impact on employment, if the number of return migrants remains high.

Access to social payments and services should be considered a protection mechanism against the negative impact of the crisis, but the observed scope and amount of social payments (with declining scope throughout the observation period of our report) are too low to compensate the observed welfare loss. Nevertheless, full implementation of new social protection rules will reduce the im-pact of the crisis on the poor and ‘’new poor’’, particularly if the programme reaches all vulnerable groups and those who are most in need are not left outside the system.

From an economic point of view we could argue that, since income and employment falls have the most significant negative impacts on welfare, action against crisis impacts should concentrate on in-come and employment generation. The link between unemployment and poverty is well established and hence sharply rising unemployment levels resulting from domestic economic contraction are likely to have negative poverty impacts. These effects may, in practice, be exacerbated since it is less feasible now than in the past for the unemployed to rely on agriculture as an informal safety net. Consequently, policies should concentrate on employment generation. In the long run this might be possible with structural changes in the economy and in the short run it calls for public works and community employment schemes (not only infrastructure investment).

Low income quintiles in rural areas are the most affected by the crisis. This deepens poverty gaps and might increase inequality (which will make future poverty reduction more difficult). The central and southern regions are particularly affected. Grouping by household types we find that the most affected are: families with migrants, households relying on pensions, households with a high number of children and families with children where the household head is not the parent. A lower available income is a threat for covering energy costs in winter (which in the end can be a health problem).

53 Appearance of “new poor” households in rural area and of “already poor” households which are falling into even deeper poverty.

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For many of the affected groups, social payments fail to compensate for income losses. Even if the amount of social payments (per unit payment) increased, the coverage decreased. There is the pos-sibility that a full implementation of Law No 133 will partially improve this situation, but since access to these benefits is based on self-selection by the households, it is worrying that by the second half of 2009 only 47 per cent of all households said they were aware of the existence of the new law and the possibility of requesting payments.

Available data does not allow a comprehensive projection of poverty rates but there is strong evi-dence of a substantial increase in poverty and that the impact below the poverty line will be much stronger than the rise in the number of poor people.

Recommendations

We will provide recommendations in two blocks, firstly regarding action that can be taken on ru-ral poverty and unemployment and secondly action in the area of social protection. Proposals are grouped into short, medium and long term periods.

Rural poverty and unemployment

Short term:

1. Public works as a temporary safety net: especially targeted at returning migrants, the young, women and Roma. These kinds of programmes have a double positive effect since they pro-vide income to households and at the same time create public benefits via, for instance, in-frastructure rehabilitation, community work or implementation of environmental protection activities.

Medium Term:

2. Improve employment and social services, support youth and self-employment: • Improve coordination of services of government, private sector and CSOs, especially at

the local level;• Improve job-readiness, help rural youth, women, and returning migrants find jobs;• Address bottlenecks in the agricultural supply chain;• Lower cost of employing young workers and encourage business start-ups;• Micro-finance services: credit, savings, insurance.

Long Term:

3. Generate sustainable rural income:• Develop the non-farm rural economy, notably agro-processing;• Provide agricultural public goods—extension services, irrigation;• Implementation of conservation agriculture.

Social protection

Short term:

4. Accelerate the roll out of the law on Social Support by simplifying the application proce-dures, improving communication targeting the most vulnerable and monitoring implementa-tion focusing on the most vulnerable;

5. Transfer or incorporate the child benefit of 50 MDL into the Social Support Scheme. This would increase the amount of funds available for Social Support by 20 Million MDL allowing the increase of the threshold of minimum state guaranteed income of 430 MDL, which is now below the absolute poverty and has no impact on poverty reduction;

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6. Reduce the number of the categories entitled for nominal compensations keeping the fol-lowing: families with four or more children under 18, families with a child with a disability (not institutionalized) and adults with disability. Although these are politically very sensitive issues, in the current financial crisis it would be easier for the government to promote this measure. This activity should be promoted with a strong communication campaign, re-orient-ing the poor beneficiaries of the current nominal compensations to Social Support scheme, and allowing to reduce the stigma surrounding applications to the Social Support scheme;

7. Prevent child malnutrition by continuing the distribution of food to pregnant women, lactat-ing mothers and children under 3 years of age during the winter;

8. Use the results of this report for to underpin the adjustment in means testing proposed in government’s recovery and stabilisation plan which would involve a gradual substitution of the social care system by a normative compensation system. Means testing has to be able to target the most vulnerable groups.

Medium Term:

9. Fully implement the National Programme for the Promotion of Integrated Social Services, fo-cusing on the development of these services in rural areas. The programme should be costed and adequate resources must be allocated. The results of the assessment show that families with many children, families with children headed by others than the parents and families with income from agriculture are most affected by the crisis. In addition, the poor are getting even poorer, which according to analysis of previous crises that hit ex-USSR countries, if not properly addressed, might lead to dramatic increase in the number of children in institutions;

10. Increase the coverage of the Early Childhood Development (ECD) programme, focusing on the most vulnerable children, as young children (from 0 to 7 years of age) are the most vul-nerable to the effects of the crisis such as malnutrition, which would jeopardize their future health, development and education. International experts have proven that ECD provides the most efficient and effective return on investment;

11. Consider the possibility of implementing conditional cash transfer programmes which can target vulnerable groups temporarily throughout the crisis and recovery period for house-holds which are not covered by social programmes and in order to ensure the continuity of the processes of capability creation in children.

Long Term:

12. Simplify the pension system, making it more transparent and linking it to contributions to the social insurance but also with the coverage of the basic needs of elderly people, many of them living with grandchildren left behind by migrating parents;

13. Within the medical insurance programmes, gradually extend the reimbursement of medi-cines for the most frequent childhood illnesses, initially up to 10 years old and at a later stage up to 15 years old. The assessment shows that families have started to spend more on health, due to the increase in cost of medication and medical services. This might lead to the risk of poor families not being able to treat children’s illnesses in a timely and proper fash-ion, resulting in these diseases becoming chronic with further high costs for families and the whole of society;

14. Redesign the coverage and content of the basic health package for the mother and child, by emphasising the most efficient and effective medical services (e.g. immunization etc);

15. Consider the possibility of adopting result oriented budgeting processes in the medium term for social policies, in order to effectively translate monetary resources into social out-comes.

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Box 6: Emergency employment – generic description

There is a wide range of policy options and measures that can be implemented under the head-ing of emergency employment, which goes far beyond public works. Many of these projects can be implemented at a community level (for an explicit example of an emergency employ-ment program please see the annex section).

• Emergencyemploymentschemescanbequicklyexpandedinacrisis(counter-cyclicalschemes) in urban areas for urban youth, retrenched workers and others, and in rural areas, especially for those who move from urban areas in search of subsistence;

• Institutionalisedapproachestoarapidexpansionofinformalsectorself-employmentand micro-enterprise activities;

• Preparing a shelf of potential schemes/business plans basedon local resources andskillswhichcanbequicklyactivated;

• Identifying NGOs, CBOs, voluntary organizations, women’s organizations (includingself-help associations in the informal sector) as partners and building their capacity;

• Examiningandupgrading institutional supportmechanisms foremergencyemploy-ment schemes (training in skills, small contractor training, contracting procedures, pro-ductivity measurement, cash wage levels, etc.) and for micro enterprises (short skill and business training, microfinance, input supply, marketing and technical support, etc.);

• Conducting trainingneedsassessments in the lightof changingdemandconditionsanddevelopingtraining/retrainingschemes;

• Strengtheningemploymentservicesandlabourmarketinformationsystemstofacilitateearly detection of downturns and to act as active labour market policy instruments;

• Strengthening community andhousehold copingmechanisms andpreventing theirdecline by strengthening existing social protection system mechanisms and develop-ing alternative channels for voluntary social protection, including:

• Identifyingandstrengtheninginformalgroupfinancesystemsasabuffermecha-nism to reduce the fragility of vulnerable households and promoting organic links between credit and savings;

• Exploringsystemsofmicrofinanceandtheirsourcesandprocedures;

• Promotingself-helpvoluntaryschemesatthecommunitylevelforsocialinsuranceand social protection.

Source: ILO Generic Crisis Response Modules - Module 2 – Financial and Economic Downturns

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Box 7: Opportunity for public works action scaling up - UNDP ILDP programme

Regarding proposed public works and emergency employment activities, important first steps have already been taken through the UNDP-implemented Integrated Local Development Pro-gramme (ILDP), focused on the central region, which could be scaled up. The key element of the ILDP has been the development of local development strategies which have comprised, as one component, the identification of priorities for infrastructure rehabilitation and devel-opment. The principal focus of actions can be on the construction, reconstruction and reha-bilitation of public infrastructure (including transport infrastructure and water systems) and public buildings. Participation in the programme, in terms of taking jobs on the public works programme, will be voluntary. Registration as unemployed will not be a requirement since it is expected that wage rates will be at a level which does not offer an incentive for those already in employment to seek jobs under the public works programme. As a result, substitution ef-fects are expected to be limited.

Participants will prospectively have access to retraining and requalification before, during or af-ter participation in the programme. The intention would be to provide skills which improve em-ployability in participants’ current professions or to provide training which would allow a change of profession. The overarching objective would be to enhance skill sets in professions which are likely to be in demand when there is an upturn in the economy. Completion of both the training and the public works attachment may also make participants eligible for a wage subsidy. This will cover health insurance and other mandatory employment costs and may also cover part of direct salary costs. The subsidy will prospectively be provided for six to 12 months, with an option to extend the subsidy for a further six months at a lower rate. The actions are chosen according to the local development strategies for the target areas. Direct Grant Agreements for the works will be concluded between UNDP Moldova and the Local Public Authorities involved which will then be the contracting authorities for the works.

Source: UNDP Moldova

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Box 8: Scaling up services under the ongoing UNDP ‘Better opportunities for women and youth’ project

Another already existing project which effectively addresses the problems highlighted in this report and which could be scaled up is the USAID-funded and UNDP-implemented programme of ‘Better Opportunities for Youth and Women’, where support is provided to the young unem-ployed who have not previously registered with the National Employment Agency. These are typ-ically either discouraged workers or workers who are unaware of the benefits that registration brings. Once contacted by the programme team (usually through the intervention of the mobile service established under the programme), they are given career counselling and guidance be-fore they select a vocational training programme (with the costs of the programme normally met by the National Employment Agency). Most participants are then placed in a post and, depend-ing on the salary paid, ‘co-financing’ is sometimes provided for up to three months with the aim of raising salaries to levels seen as giving incentive to work.

In practice, the services and support provided to the young unemployed by the Social Reintegra-tion Centres go beyond that currently available from the National Employment Agency. Thus, the Social Reintegration Centres’ clients receive enhanced job counselling and, almost certainly, more personalised attention in choice of training programmes. There is a case for enabling the Social Reintegration Centres to increase the numbers of young clients they address. The proc-esses they have installed have been shown to work, if only because, through demand for their services, they have expanded coverage beyond their original remit of disadvantaged youth to embrace a wider target audience of the unemployed young. And given the probable expansion in the need and demand for their services which would arise from sharp increases in youth unem-ployment, they can potentially provide an important ‘emergency’ response as the crisis unfolds and has impacts on the young.

In the longer term it will be important for the National Employment Agency to be able to address all the unemployed young. As recognised in the NEA’s development strategy, taking on this task in full is likely to require a significant increase in capacity and linked improvement in capability. And while the Social Reintegration Centres are able, in the context of ‘Better Opportunities for Youth and Women’ to provide a parallel service which meets needs that the NEA cannot cur-rently address, it will be important for these functions gradually to be incorporated into the NEA. Given that many of the actions foreseen in the NEA strategy (including individualised services for young people, mobile teams and targeting rural areas) have been successfully tested and imple-mented by the Social Reintegration Centres, and given the NEA strategy’s emphasis on this as a public function, there is thus a case for actions enabling their progressive transfer to the NEA’s responsibility and hence for their becoming part of the agency’s mainstream tasks. The intention would be thus be to formalise procedures and approach as a basis for transfer on a pilot basis to selected local offices of the NEA.


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Box 9: Support to start-ups in food processing

The objective of the assistance in the framework of this third programme is to support food processing start-ups such that they can begin trading and can graduate to receive loan assist-ance and other support available to small enterprises. Activities, which will be in the areas of the central region (one of the regions most affected by the crisis, as the study notes) working, as appropriate, with the SME Development Agency (‘ODIM’), will comprise:

Assessment of the technical and financial feasibility of small-scale food processing. Despite extensive discussion of small-scale food processing in transitional countries, little is known about its feasibility in Moldova and there is limited accumulated experience. The assessment of feasibility will focus on the availability and scalability of technology and on financial feasi-bility. The latter will of course require market analyses;

Assessments of feasibility would therefore be conducted, comprising analysis of the following:

- Estimates of volumes and timing of primary agricultural produce available for processing;

- Determination of the processed food products that might be produced at the local and regional level from available raw materials;

- Identification of market opportunities, determining in the process product characteristics and likely volumes in both domestic and export markers;

- Identification and costing of technology suitable for processing at the household, micro enterprise and cooperative level and estimation of minimum volumes for commercial production.

A key objective will be the identification of the support that would be required to enable the growth of food processing at the household, micro enterprise and cooperative level.

Support to existing small-scale processors. Tailored, individual business advisory support pro-vided to emerging small-scale processors in the central region. Delivered through existing business advisory services and other projects, the aim will be for them quickly to benefit from loan funding available for small enterprises. They would receive support with business management, product development and marketing, and technology;

Installation of food microprocessing facilities. Facilities of this type have emerged in the Unit-ed States, Canada, Ireland and the UK as a way to stimulate business development in both rural and urban areas. Most foods produced for sale cannot be processed (cooked, canned, frozen, packaged or otherwise altered for sale and consumption) in a typical home kitchen. Rather, in order to sell prepared food, it must be processed in a kitchen that meets conditions of layout, equipment, sanitation, and management and passes regular inspection. It is also important that the kitchen is of a size which allows commercial volumes to be processed.

Technology diffusion by organising trade fairs with the aim of bringing processors and suppli-ers together and so improving processors’ knowledge of available technology. The aim will be to field a diversity of products from different manufacturers and the fairs will mainly be held in rural areas to avoid small-scale processors having to travel long distances.


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Box 10: Conditional Cash Transfer Programmes

Unlike most development initiatives, conditional cash transfer programs recently introduced in Latin America and the Caribbean have been subject to rigorous evaluations of their effectiveness. These programs provide money to poor families conditional upon certain behaviour, usually in-vestments in human capital such as sending children to school or bringing them to health centers on a regular basis. Evaluations are available for programs launched in Mexico, Brazil, Honduras, Jamaica and Nicaragua. Evaluation results from the first generation of programs in Mexico, Brazil and Nicaragua show that conditional cash transfer programs are an effective means for promot-ing human capital accumulation among poor households. There is clear evidence of success in increasing enrolment rates (or avoiding school dropouts), improving preventive health care and raising household consumption.

Child health and nutrition has improved as a result of CCT programs. The PROGRESA evaluation shows a significant increase in nutrition monitoring and immunisation rates. Infants under three years old participating in PROGRESA increased their growth monitoring visits between 30 to 60 percent, and beneficiaries aged 0 to 5 had a 12 per cent lower incidence of illness compared to non-PROGRESA children. In addition, the data suggest that PROGRESA has had a significant im-pact on increasing child growth and lowered the probability of child stunting for children aged 12 to 36 months old. In Nicaragua, even greater improvements were generated by the CCT program. Approximately 60 percent of children aged fewer than 3 participated in nutrition monitoring before the RPS was implemented. After a few months of program operation, more than 90 per-cent of children in RPS areas benefited from nutrition monitoring compared to only 67 percent in control areas. In terms of immunisation rates, the RPS increased timely immunisation among children aged between 12 and 23 months old by 18 percentage points.

Consumption levels have also improved as a result of participating in CCT programs. In Mexico, the average consumption level of PROGRESA households increased by 14 per cent, and median food expenditures after just over a year of program operation were 11 per cent higher compared to non-PROGRESA households. The increase in household consumption is in large part driven by higher expenditures on fruits, vegetables, and animal products. Median calorie acquisition in PROGRESA households increased by 7.8 per cent. In Nicaragua, control households experienced a sharp decline in consumption due in part to low coffee prices and a drought, whereas the av-erage per capita annual household expenditures in RPS areas did not change. The net program impact translates into a 19 per cent increase in per capita consumption and suggests that CCT programs may help poor people protect consumption in times of crisis, a risk management role worthy of further analysis.

Source: World Bank - Rawlings, Laura and Gloria Rubio (2003)

A frequent criticism made against CCT programs is their supposed inefficiency. Box 7 shows some existing experiences regarding CCT programs. Weather a CCT program should be considered ineffi-cient or not depends on the concept behind them. If the idea is that households that benefit should overcome their disadvantages in terms of income- or expenditure-defined measures of poverty by receiving monthly cash transfers, the programmes will of course be inefficient because most prob-ably the transfers will be insufficient. But if the goal of the programmes is to enable lower income households to continue the processes of capacity creation in the households even in crisis periods and protect existing capabilities, CCT programmes seem to be sufficiently efficient and should be considered as a policy alternative especially because there is already statistical evidence regarding a decrease in pre-primary school enrolment and regarding difficulties in access to health services for lower income households.

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The social impact of crisis can be long-lasting since social issues recover only in the third stage (after growth recovery and employment recovery). There is empirical evidence from former crises (world-wide) that employment recovery takes twice as long as production recovery. For Moldova, this would mean employment recovery in 2011 and social recovery in 2012. Important changes below the pov-erty line happening now (inequality increases) will make social recovery more complex and slower.

Protection of human capital during the crisis should be the main focus of governmental interven-tions to mitigate the impact of crisis. Debt-financed intervention should be considered. This goal of protection could be reached through more flexible or extended access conditions to (temporary) so-cial payments (conditional cash transfers), better targeting of social payments which could go hand in hand with the development of social services at the community level. Therefore, collection of data on the existing social services needs to be informed by an assessment of need in each community. The new government should focus on these social programmes, which might have a better impact on the most vulnerable families, like subsidies for heating and water etc., revise targeting of social payments, revise fiscal spaces for debt financing, consider microcredit programs for urban areas, provide financial opportunities for SMEs and consider the possibility of implementing conservation agriculture techniques to increase incomes from small-scale rural agriculture.

Box 11: Conservations agriculture

Conservation Agriculture is gaining acceptance in many parts of the world as an alternative to both conventional agriculture and to organic agriculture. Although the practice of conservation agriculture on a large scale emerged out of Brazil and Argentina, similar developments had been taking place in many other areas of the world, notably in North America with zero tillage, and in Africa and Asia with technologies such as agroforestry. Conservation agriculture is based on the principles of rebuilding the soil, optimising crop production inputs, including labour, and optimiz-ing profits.

Conservation agriculture is the integration of ecological management with modern, scientific, agricultural production. Conservation agriculture employs all modern technologies that enhance the quality and ecological integrity of the soil, but the application of these is tempered with tra-ditional knowledge of soil husbandry gained from generations of successful farmers.

Conservation agriculture promotes minimal disturbance of the soil by tillage (zero tillage), bal-anced application of chemical inputs (only as required for improved soil quality and healthy crop and animal production), and careful management of residues and wastes. This reduces land and water pollution and soil erosion, reduces long-term dependency on external inputs, enhances environmental management, improves water quality and water use efficiency, and reduces emis-sions of greenhouse gases through reduced use of fossil fuels. Conservation agriculture, includ-ing agroforestry (Figs. 1 and 2), specialty crops, and permanent cropping systems, promotes food sufficiency, poverty reduction, and value added production through improved crop and animal production, and production in relation to market opportunities. Reduced tillage leads to less-ened human inputs, in both time and effort.

Conservation agriculture is best achieved through community-driven development processes whereby local communities and farmers’ associations identify and implement the best options for CA in their location. Local, regional and national farmer associations, working through com-munity workshops, farmer-to-farmer training, etc., but with technical backstopping from conser-vation professionals, are the main players in the promotion of CA.

Source: Dumanski, J., et al (2006): The Paradigm of Conservation Agriculture

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10. References

ADEPT &EXPERT-GRUP (2009). Implementarea reformelor iniţiate conform Planului de acţiuni RM-UE, evalu-area progresului în perioada ianuarie–martie 2009. Dezvoltare şi reforme economice. EUROMONITOR nr. 5(14) Ediţia IV, http://www.expert-grup.org/library_upld/d208.pdf

ADEPT &EXPERT-GRUP (2009). Implementarea reformelor iniţiate conform Planului de acţiuni RM-UE, eva-luarea progresului în perioada aprilie–iunie 2009. Dezvoltare şi reforme economice. EUROMONITOR nr. 6(15) Ediţia V., http://www.expert-grup.org/library_upld/d210.pdf

ANOFM (2009). Numărul persoanelor aflate în cautarea unui loc de muncă. Raport statistic aprilie 2009. http://www.anofm.md/Sites/anofm_md/Uploads/raport%20statistic%2011%20mai.F201003B4B1F4AAE8A-24F39ED3142E0E.doc

ANRE (2009). Raportul anual de activitate a ANRE în anul 2008. Activităţile de actualizare a tarifelor la ga-zele naturale, la energia electrică şi termică. http://www.anre.md/upl/file/Rapoarte/Raport%20anual%202008%20total.doc

Aplostol, P., Republica Moldova între vis şi realitate. 2009, http://petruapostol.wordpress.com/2009/08/01/republica-moldova-intre-vis-si-realitate/

Chivriga, V., Evoluţia pieţii funciare agricole în Republica Moldova. IDIS - Politici Publice nr. 4 2009. http://www.viitorul.org/public/2164/ro/Evolutia%20Pietei%20Funciare.pdf

Dumanski, J., et al (2006): The Paradigm of Conservation Agriculture. http://www.unapcaem.org/publication/ConservationAgri/ParaOfCA.pdf

ILO (2001): ILO Generic Crisis Response Modules - Module 2 – Financial and Economic Downturns

IMF (2009). Press Release No. 09/206: June 10, 2009

IDIS (2009). Economic Statewatch. Quaterly Analisis and Forecasts Issue 15. Q 1 http://www.viitorul.org/pub-lic/1786/en/ME_1_2009_ENG.pdf

IMF World Economic Outlook Update October 2009

IMF Press Release No. 09/206: June 10, 2009

IOM: The Socio-Economic Impact of the Economic Crisis on Migration and Remittances in the Republic of Moldova, April 2009

IOM, Labour Migration and Remittances in Moldova: Is the Boom Over?”

IOM/CBS-AXA, 2008, Labor Migration and Remittances in Moldova, 20062008: Trends and Preliminary Find-ings from the Panel Household Survey

MET (2007). The Report on Poverty and Policy Impact;

MET (2009). The social-economic situation in Moldova in january - march 2009. http://www.mec.gov.md/files/documents/Evolutia%204%20luni%202009.pdf

MSPFC (2009). Annual Social Report 2008 (draft).

NBS. Indicile preţurilor protucţiei agricole. www.statistica.md/public/files/serii_de_timp/preturi/preturi_ag-ricultura/1_ind_trim_pret_agric_2000_2009.xls

NBS. Forţa de muncă în Republica Moldova: ocuparea şi şi şomajul în trimestrul I 2009.

NBS. Structura populaţiei stabile, pe sexe şi vârste, la începutul anului 2009. http://www.statistica.md/news-view.php?l=ro&idc=168&id=2602

NBS (2009). Rezultatele studiului privind Sănătatea şi accesul populaţiei la serviciile de sănătate în Republica Moldova. Realizat la solicitarea MS cu suportul WB.

NBS. Veniturile şi cheltuielile populaţiei în trimestrul I 2009. http://www.statistica.md/newsview.php?l=ro&idc =168&id=2667

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NBS (2009). Situaţia social-economică a Republicii Moldova în ianuarie – martie 2009. http://www.statistica.md/public/files/publicatii_electronice/Raport_trimestrial/Raport_I_2009_ROM.pdf

NBS(2009). Situaţia social-economică a Republicii Moldova în ianuarie – iunie 2009. http://www.statistica.md/public/files/publicatii_electronice/Raport_trimestrial/raport_II_2009_rom.pdf

NBS (2009). Forţa de muncă în Republica Moldova: ocuparea şi şomajul în trimestrul I 2009. http://www.statistica.md/newsview.php?l=ro&idc=168&id=2607

NBS (2009) Distribution of employment by activity and sex.. http://www.statistica.md/public/files/serii_de_timp/forta_de_munca/populatie_ocupata/infraanuale/Distr_pop_ocup_activ_sexe_trim.xls

Ratha and Shaw (2007) Bilateral Estimates of Migrants Stocks Database, South-South Migration and Remit-tances. World Bank, Washington, D.C.

Rawlings, Laura and Gloria Rubio (2003) Evaluating the Impact of Conditional Cash Transfer Programs - Les-sons from Latin America; World Bank Policy Research Working Paper 3119.

Sauwalak Kittiprapas, 2002, Social Impacts of Financial and Economic Crisis in Thailand, EADN Regional Project on the Social Impact of the Asian Financial Crisis

UNDP/Expert-Grup report on “Impact of global financial crisis on local communities in Moldova”, August 2009.

World Bank, 2009, MOLDOVA: The Consequences of Several Shocks for Consumption and Poverty Report No. 49019-MD

WB (2009). Global Crisis Pushing Almost 35 Million People Back into Poverty and Vulnerability in Europe and Central Asia. http://www.worldbank.org.md/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/MOLDOVAEXTN/0,,contentMDK:22165459~menuPK:50003484~pagePK:2865066~piPK:2865079~theSitePK:302251,00.html

World Bank (2005), Enhancing Job Opportunities: Eastern Europe and the Former Soviet Union.

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11. AnnexList of tables and Figures in the Annex

Table A1: Household group characteristics by household size

Table A2: Household group characteristics by sex of household head

Table A3: Household characteristics by socio economic status of household head

Table A4: Household characteristics by income quintiles

Table A5: Profile of households with children

Table A6: Monthly average per capita income by period and urban/rural area

Table A7: Monthly average per capita income by period and number of children up to 18

Table A8: Monthly average per capita income by period and education level of household head

Table A9: Monthly average per capita income by period and gender of household head

Table A10: Distribution of population by quintiles of incomes by periods (%)

Table A11: Distribution of population by quintiles of consumption by periods (%)

Table A12: Percentage change of income by geographical zone (%)

Table A13: Percentage change of income by HH size (%)

Table A14: Income change by sex of household head – before and in crisis (%)

Table A15: Income change household heads occupational status – before and in crisis (%)

Table A16: Per capita expenditures by Geographical zone

Table A17: SADI index for 3 person HH in Central and South regions, rural area

Table A18: SADI index for > 4 person HH in Central and South regions, rural area

Table A19: SADI index for HH with children up to 16 in Central and South regions, rural area

Table A20: SADI index for HH with 1 child up to 16 in Central and South regions, rural area

Figure A1: Correlation plot between per capita consumption change (% before and in crisis) and complete SADI index

Figure A2: Correlation plot between per capita consumption change (% before and in crisis) and Income deprivation component of SADI index

Figure A3: Correlation plot between per capita consumption change (% before and in crisis) and Demo-graphic deprivation component of SADI index

Figure A4: Correlation plot between per capita consumption change (% before and in crisis) and Economic deprivation component of SADI index

Figure A5: Correlation plot between per capita consumption change (% before and in crisis) and Health deprivation component of SADI index

Figure A6: Correlation plot between per capita consumption change (% before and in crisis) and Education deprivation component of SADI index


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Plane II – Experiences from an Emergency Employment Program in Tarjia - Bolivia

Plane II: Project administrated by UNDP in Bolivia, whose main purpose was the creation of massive temporary employment throughout the country and was funded by bilateral (Belgium, The Neth-erlands, Sweden and USAID) and multilateral (World Bank and CAF) agencies. This programme was created under the tuition of the Board of Funds (DUF) as a counter-cyclical project due to the alarm-ing unemployment indicators in the country. The people who benefited from this type of employ-ment belonged to the poorest strata of Bolivian society and were considered unqualified workers.

Actors involved

Board of Funds/ Prefecture of Tarija: Government institution, PLANE II and PEU, in charge of funding and monitoring the employment plans.

UNDP Bolivia: As administration agency for PLANE II from March 2003 to April 2004. Its main tasks were to administer the economic resources for the project, monitor and control the supervision and contracting companies, and to approve and monitor the implementation of the projects.

Contracting Companies: These were private microfinance institutions hired to register, contract, and pay the workers.

Supervision Companies: Private consulting companies hired to supervise the workers assistance and the technical quality of the projects, as well as transfer how to knowledge to professionals and workers

Workers: The Workers belonged to the poorest strata of society and were considered unqualified la-bourers. These people worked five days a week, seven hours a day, for one to two and a half months, and received bi-weekly payments (25 to 30 workers per project).

Project Solicitants: The Municipal and Departmental Governments, as well as other government agencies identified, prepared and presented projects to be financed by the programs based on the neighbourhood and community needs. The solicitants are also responsible for the provision of con-struction materials and tools, because the programs only finance salaries.

Implementation

The implementation of the programs is based on the outsourcing of all its components. This is why the administration, supervision and contracting responsibilities were given to different entities. All the controls are ex-post, this allows for the fast and effective disbursement of funds directly to the workers on a bimonthly basis. In other words, if the supervisor makes a mistake in the pay roll the cost is deducted from its commissions, the same applies for the contracting agency, in case a mis-take is made in the payment to the workers, and to the administrator if a mistake is made in the reimbursement process. Therefore each entity assumes full financial responsibility for its actions and errors.

• The supervision is paid a commission for each payroll it presented;• The contracting agencies work with a rotary fund and is given a commission for each worker

it pays;• This method of payment reduces fixed costs resulting on low administrative costs.

Urban project menu• Maintenance and improvement of Streets and Avenues• Prevention of Natural Disasters• Reconstruction and clean up after the occurrence natural disasters• Protection of the Environment• Sport and recreational areas

73

• Rehabilitation of Historical Buildings• Other projects with use of intense labour

Rural project menu• Maintenance and improvement of Roads• Rehabilitation of Tourism Paths• Construction and rehabilitation of Irrigation Channels• Construction and refurbishing of Classrooms• Improvement of Community Spaces• Prevention of Natural Disasters

Professional and Technical Assistance Program:

This Program hired people with a technical or professional degree in order to assist in:• The elaboration of PLANE projects.• As on site supervisors and part of the onsite training process under the premise of learning

by doing.• In charge of the assistance verification and validation

Projects 2008

Basic Services 27 6%

Disaster mitigation 55 13%

Agriculture 158 38%

Livestock 51 12%

Fishery 15 4% 4%

Fruit Harvesting 13 3%

Forestry 24 6%

Tourism 14 3%

Artisanal 2 0%

Capacity building and job insertion 8 2%

Others 52 12%

Total 419 100%

Source: UNDP Bolivia

Plane II had proved to be a very effective instrument in the recovery and reconstruction stages of disaster situations. The program has the ability to assign personnel in a very fast and effective way, therefore providing manpower for the cleanup and basic infrastructure rebuilding almost immedi-ately after the event. This type of programs can be used to hire the people affected by the disaster situation, making then an active part in the recovery process and allowing them to help themselves. The salary given to them, even though it is a national minimum salary, allows them to face the post disaster in a more dignified way by providing them with economic means for their basic needs. Due to the fact that disasters occur in an unexpected way it is important to establish a mechanism that can be launch a recovery program into action fast, the outsourcing the components to specialized instances and ex post revision procedures make this a very effective program for these situations.

74

Table A1: Household group characteristics by household size

Household size (grouped)

1-2 persons 3 4 5 and moreAge of household head (grouped)

Up to 30 years 179 5.9% 124 10.7% 65 6.2% 23 3.3%

30--39 147 4.9% 254 21.9% 285 27.0% 150 21.4%

40--49 366 12.2% 330 28.4% 375 35.5% 197 28.1%

50--59 762 25.3% 291 25.1% 210 19.9% 170 24.3%

60--64 288 9.6% 52 4.5% 32 3.0% 34 4.9%

65+ 1270 42.2% 110 9.5% 88 8.3% 127 18.1%

Total 3012 100.0% 1161 100.0% 1055 100.0% 701 100.0%

Availability of children up to 16

HH with children up to 16

169 5.6% 619 53.3% 778 73.7% 641 91.4%

HH without children up to 16

2843 94.4% 542 46.7% 277 26.3% 60 8.6%

Total 3012 100.0% 1161 100.0% 1055 100.0% 701 100.0%

Number of children up to 16


167 5.5% 495 42.6% 353 33.5% 182 26.0%


2 .1% 123 10.6% 401 38.0% 251 35.8%


1 .1% 24 2.3% 208 29.7%


2843 94.4% 542 46.7% 277 26.3% 60 8.6%

Total 3012 100.0% 1161 100.0% 1055 100.0% 701 100.0%

Type of household (up to 16)

Lonely person 1329 44.1%

Couple without children

1124 37.3%

Couple with children up to 16

349 30.1% 349 33.1% 99 14.1%


87 2.9% 50 4.3% 10 .9% 2 .3%


81 2.7% 220 18.9% 419 39.7% 540 77.0%

Other HH without children

391 13.0% 542 46.7% 277 26.3% 60 8.6%

Total 3012 100.0% 1161 100.0% 1055 100.0% 701 100.0%

Strate Cities 508 16.9% 245 21.1% 199 18.9% 78 11.1%

Towns 603 20.0% 247 21.3% 213 20.2% 87 12.4%

Villages 1901 63.1% 669 57.6% 643 60.9% 536 76.5%

Total 3012 100.0% 1161 100.0% 1055 100.0% 701 100.0%

Source: NBS; HBS I 2009, own data processing.

75

Table A2: Household group characteristics by sex of household head

Sex of household head

Male (N/%) Female (N/%)

Age of household head (grouped)

Up to 30 years 230 6.5 161 6.8

30--39 565 15.9 271 11.4

40--49 861 24.2 407 17.2

50--59 903 25.4 530 22.4

60--64 243 6.8 163 6.9

65+ 757 21.3 838 35.4

Total 3.559 100 2.370 100

Socio-economic status of household

Farmers 667 18.7 166 7.0

Employees in agricultural sector 282 7.9 66 2.8

Employees in non-agricultural sector 1.113 31.3 647 27.3

Entrepreneurs 22 0.6 9 0.4

Pensioners 990 27.8 1.149 48.5

Other 485 13.6 333 14.1

Total 3.559 100 2.370 100


Lonely person 354 9.9 975 41.1

Couple without children 997 28.0 127 5.4

Couple with children up to 16 721 20.3 76 3.2

Single parent with children up to 16 18 0.5 131 5.5

Other HH with children up to 16 708 19.9 552 23.3

Other HH without children 761 21.4 509 21.5

Total 3.559 100 2.370 100

Availability of children up to 16

HH with children up to 16 1.448 40.7 759 32.0

HH without children up to 16 2.111 59.3 1.611 68.0

Total 3.559 100.0 2.370 100

0 - without, 1- with migrants0.00 3.116 87.6 1.824 77.0

1.00 443 12.4 546 23.0

Total 3.559 100 2.370 100.0

Households 0 - without invalids, 1- with invalids

0.00 3.028 85.1 2.101 88.6

1.00 531 14.9 269 11.4

Total 3.559 100.0 2.370 100


76

Table A3: Household characteristics by socio-economic status of household head


Farmers (N/%)

Employees in agric. sector

(N/%)

Employees in non-agric. sector (N/%)

Entrepreneurs (N/%)

Pensioners (N/%)

Other (N/%)

LocationUrban 43 5.2 35 10.1 1.082 61.5 16 51.6 633 29.6 371 45.4Rural 790 94.8 313 89.9 678 38.5 15 48.4 1.506 70.4 447 54.6

Total 833 100 348 100 1.760 100 31 100 2.139 100 818 100

StrateCities 3 0.4 4 1.1 583 33.1 8 25.8 255 11.9 177 21.6Towns 40 4.8 31 8.9 499 28.4 8 25.8 378 17.7 194 23.7Villages 790 94.8 313 89.9 678 38.5 15 48.4 1.506 70.4 447 54.6

Total 833 100 348 100 1.760 100 31 100 2.139 100 818 100

Geographical zone

North 287 34.5 117 33.6 425 24.1 11 35.5 792 37.0 276 33.7Center 340 40.8 104 29.9 450 25.6 6 19.4 662 30.9 255 31.2South 196 23.5 119 34.2 308 17.5 4 12.9 463 21.6 143 17.5Chisinau 10 1.2 8 2.3 577 32.8 10 32.3 222 10.4 144 17.6

Total 833 100 348 100 1.760 100 31 100 2.139 100 818 100

0 - without, 1- with migrants

0.00 695 83.4 288 82.8 1.490 84.7 27 87.1 1.930 90.2 510 62.31.00 138 16.6 60 17.2 270 15.3 4 12.9 209 9.8 308 37.7

Total 833 100.0 348 100 1.760 100 31 100 2.139 100 818 100

Education level of household head (grouped)

Without primary 3 0.4 122 5.7 6 0.7

Primary or incomplete secondary

201 24.1 100 28.7 136 7.7 1.137 53.2 134 16.4

Secondary 602 72.3 227 65.2 1.159 65.9 19 61.3 699 32.7 629 76.9Higher 27 3.2 21 6.0 465 26.4 12 38.7 181 8.5 49 6.0

Total 833 100 348 100 1.760 100 31 100 2.139 100 818 100



181 21.7 76 21.8 480 27.3 6 19.4 215 10.1 239 29.2


154 18.5 49 14.1 273 15.5 6 19.4 119 5.6 176 21.5


61 7.3 23 6.6 48 2.7 1 3.2 37 1.7 63 7.7


437 52.5 200 57.5 959 54.5 18 58.1 1.768 82.7 340 41.6

Total 833 100 348 100 1.760 100 31 100 2.139 100 818 100

Household size (grouped)

1-2 persons 298 35.8 146 42.0 645 36.6 9 29.0 1.609 75.2 305 37.33 176 21.1 72 20.7 490 27.8 6 19.4 201 9.4 216 26.44 191 22.9 70 20.1 436 24.8 13 41.9 154 7.2 191 23.35 and more 168 20.2 60 17.2 189 10.7 3 9.7 175 8.2 106 13.0

Total 833 100 348 100 1.760 100 31 100 2.139 100 818 100


Up to 30 years 35 4.2 15 4.3 160 9.1 1 3.2 5 0.2 175 21.430--39 156 18.7 51 14.7 378 21.5 8 25.8 16 0.7 227 27.840--49 297 35.7 102 29.3 549 31.2 8 25.8 67 3.1 245 30.050--59 279 33.5 159 45.7 551 31.3 13 41.9 281 13.1 150 18.360--64 35 4.2 18 5.2 101 5.7 1 3.2 242 11.3 9 1.165+ 31 3.7 3 0.9 21 1.2 1.528 71.4 12 1.5

Total 833 100 348 100 1.760 100 31 100 2.139 100 818 100


77

Table A4: Household characteristics by income quintiles

Q I (N/%) Q II (N/%) Q III (N/%) Q IV (N/%) Q 5 (N/%)


Up to 30 years 60 5.8 46 3.9 57 4.5 69 5.7 159 12.930--39 191 18.5 142 12.0 130 10.3 175 14.4 198 16.140--49 289 28.0 202 17.0 201 16.0 280 23.0 296 24.050--59 279 27.0 235 19.8 259 20.6 298 24.5 362 29.460--64 56 5.4 76 6.4 97 7.7 85 7.0 92 7.565+ 158 15.3 486 40.9 516 41.0 310 25.5 125 10.1

Total 1.033 100 1.187 100 1.260 100 1.217 100.0 1.232 100


Farmers 404 39.1 147 12.4 111 8.8 104 8.5 67 5.4Employees in agricultural sector

100 9.7 71 6.0 60 4.8 58 4.8 59 4.8

Employees in non-agricultural sector

145 14.0 236 19.9 294 23.3 459 37.7 626 50.8

Entrepreneurs 3 0.3 2 0.2 3 0.2 6 0.5 17 1.4Pensioners 258 25.0 608 51.2 654 51.9 411 33.8 208 16.9Other 123 11.9 123 10.4 138 11.0 179 14.7 255 20.7

Total 1.033 100 1.187 100 1.260 100.0 1.217 100.0 1.232 100


HH with 1 children 256 24.8 217 18.3 213 16.9 269 22.1 242 19.6HH with 2 children16 222 21.5 169 14.2 155 12.3 140 11.5 91 7.4HH with 3 and more children

115 11.1 54 4.5 34 2.7 20 1.6 10 0.8

HH without children 440 42.6 747 62.9 858 68.1 788 64.7 889 72.2Total 1.033 100 1.187 100 1.260 100 1.217 100 1.232 100


Lonely person 118 11.4 300 25.3 347 27.5 253 20.8 311 25.2Couple without children 110 10.6 243 20.5 277 22.0 227 18.7 267 21.7Couple with children up to 16

200 19.4 138 11.6 144 11.4 162 13.3 153 12.4


40 3.9 26 2.2 25 2.0 31 2.5 27 2.2


352 34.1 276 23.3 233 18.5 236 19.4 163 13.2

Other HH without children

213 20.6 204 17.2 234 18.6 308 25.3 311 25.2

Total 1.033 100 1.187 100 1.260 100 1.217 100 1.232 100Availability of children up to 16

HH with children < 17 593 57.4 440 37.1 402 31.9 429 35.3 343 27.8HH without child. < 17 440 42.6 747 62.9 858 68.1 788 64.7 889 72.2

Total 1.033 100 1.187 100 1.260 100 1.217 100 1.232 100

MigrantsWithout 900 87.1 1.049 88.4 1.089 86.4 995 81.8 907 73.6With 133 12.9 138 11.6 171 13.6 222 18.2 325 26.4

Total 1.033 100 1.187 100 1.260 100 1.217 100 1.232 100

InvalidsWithout 910 88.1 1.018 85.8 1.066 84.6 1.036 85.1 1.099 89.2With 123 11.9 169 14.2 194 15.4 181 14.9 133 10.8

Total 1.033 100 1.187 100 1.260 100 1.217 100 1.232 100

LocationUrban 199 19.3 361 30.4 383 30.4 535 44.0 702 57.0Rural 834 80.7 826 69.6 877 69.6 682 56.0 530 43.0

Total 1.033 100 1.187 100 1.260 100 1.217 100 1.232 100

StrataCities 63 6.1 123 10.4 141 11.2 256 21.0 447 36.3Towns 136 13.2 238 20.1 242 19.2 279 22.9 255 20.7Villages 834 80.7 826 69.6 877 69.6 682 56.0 530 43.0

Total 1.033 100 1.187 100 1.260 100 1.217 100 1.232 100

Geographical zone

North 328 31.8 422 35.6 455 36.1 384 31.6 319 25.9Center 417 40.4 379 31.9 406 32.2 330 27.1 285 23.1South 251 24.3 281 23.7 270 21.4 244 20.0 187 15.2Chisinau 37 3.6 105 8.8 129 10.2 259 21.3 441 35.8

Total 1.033 100 1.187 100 1.260 100 1.217 100 1.232 100


78

Table A5: Profile of households with children

Before crisis In Crisis TOTAL

Count Row % Column % Count Row % Column

% Count Row % Column %

Geographical zone

North 379 52.6 28.8 342 47.4 28.5 721 100 28.7Center 410 50.6 31.2 401 49.4 33.4 811 100 32.3South 302 54.1 23.0 256 45.9 21.4 558 100 22.2Chisinau 224 52.8 17.0 200 47.2 16.7 424 100 16.9

StrateCities 233 52.6 17.7 210 47.4 17.5 443 100 17.6Towns 250 52.4 19.0 227 47.6 18.9 477 100 19.0Villages 832 52.2 63.3 762 47.8 63.6 1 594 100 63.4

Sex of household head

Male 856 52.4 65.1 779 47.6 65.0 1 635 100 65.0

Female 459 52.2 34.9 420 47.8 35.0 879 100 35.0

Type of households (children up to 18)

Couple with children 542 52.8 41.2 485 47.2 40.5 1 027 100 40.9

Single parent with children 117 52.9 8.9 104 47.1 8.7 221 100 8.8

Other HH with children 654 51.8 49.7 608 48.2 50.7 1 262 100 50.2


HH with 1 children 667 53.7 50.7 576 46.3 48.0 1 243 100 49.4HH with 2 children 483 50.8 36.7 468 49.2 39.0 951 100 37.8

HH with 3 and more children 165 51.6 12.5 155 48.4 12.9 320 100 12.7

Socio-economic status of household head

Farmers 253 57.1 19.2 190 42.9 15.8 443 100 17.6

Employees in agric- sector 92 54.1 7.0 78 45.9 6.5 170 100 6.8

Employees in non-agric. sector 506 54.6 38.5 420 45.4 35.0 926 100 36.8

Entrepreneurs 5 33.3 0.4 10 66.7 0.8 15 100 0.6Pensioners 231 54.7 17.6 191 45.3 15.9 422 100 16.8Other 228 42.4 17.3% 310 57.6 25.9 538 100 21.4

Education level of household head (grouped)

Without primary 15 68.2 1.1 7 31.8 0.6 22 100 0.9

Primary or incomplete secondary

299 54.3 22.7 252 45.7 21.0 551 100 21.9

Secondary 835 50.8 63.5 808 49.2 67.4 1 643 100 65.4Higher 166 55.7 12.6 132 44.3 11.0 298 100 11.9


Up to 30 years 126 50.2 9.6 125 49.8 10.4 251 100 10.030—39 years 380 51.6 28.9 357 48.4 29.8 737 100 29.340—49 years 390 51.2 29.7 372 48.8 31.0 762 100 30.350—59 years 234 54.8 17.8 193 45.2 16.1 427 100 17.060—64 years 47 53.4 3.6 41 46.6 3.4 88 100 3.565+ years 138 55.4 10.5 111 44.6 9.3 249 100 9.9

Migrants in HHwithout mig. 986 52.1 75.0 908 47.9 75.7 1 894 100 75.4with migrants 328 53.0 25.0 291 47.0 24.3 619 100 24.6


79

Table A6: Monthly average per capita income by period and urban/rural area


Urban Rural

Before crisis In crisis Before crisis In crisis

MDL % MDL % MDL % MDL %

Monthly disposable income per capita 1 169 100.0 1 217 100.0 796 100.0 728 100.0

Income from employment 710 60.7 748 61.5 240 30.2 197 27.1

Income from self-employment in agriculture 14 1.2 11 0.9 196 24.6 148 20.3

Income from self-employment in non-agriculture 131 11.2 110 9.0 47 5.9 61 8.4

Income from property 5 0.4 6 0.5 1 0.1 2 0.3

Social payments 66 5.6 70 5.8 62 7.8 67 9.2

Pensions 43 3.7 43 3.5 43 5.4 47 6.5

child allowances 9 0.8 15 1.2 6 0.8 6 0.8

family compensations 3 0.3 3 0.2 4 0.5 5 0.7

Other incomes 243 20.8 273 22.4 250 31.4 252 34.6

remittances 159 13.6 208 17.1 227 28.5 230 31.6

Obs: In 2007 Oct prices; Source: NBS; HBS I 2009, own data processing.

Table A7: Monthly average per capita income by period and number of children up to 18


HH with 1 child up to 18



Before crisis In crisis Before crisis In crisis Before crisis In crisis

MDL % MDL % MDL % MDL % MDL % MDL %

Monthly disposable income per capita 1 095 100.0 1 024 100.0 903 100.0 947 100.0 600 100.0 603 100.0

Income from employment 545 49.8 507 49.5 378 41.9 428 45.2 188 31.3 159 26.3

Income from self-employment in agriculture 115 10.5 94 9.2 125 13.8 87 9.2 156 26.1 104 17.3


87 7.9 66 6.5 86 9.5 90 9.5 47 7.8 97 16.1

Income from property 1 0.1 5 0.5 4 0.5 1 0.2 4 0.7 5 0.8

Social payments 75 6.8 81 7.9 57 6.3 61 6.4 48 8.1 52 8.7

pensions 54 5.0 60 5.8 39 4.3 39 4.1 23 3.8 23 3.9

child allowances 6 0.5 6 0.6 7 0.8 12 1.3 11 1.8 13 2.1

family compensations 4 0.3 5 0.4 3 0.3 3 0.3 4 0.7 6 1.0

Other incomes 273 24.9 271 26.4 253 28.1 279 29.5 157 26.1 185 30.7

remittances 217 19.8 219 21.4 210 23.2 247 26.1 134 22.3 163 26.9


80

Table A8: Monthly average per capita income by period and education level of household head


Primary or incomplete secondary Secondary Higher

Before crisis In crisis Before crisis In crisis Before crisis In crisis

MDL % MDL % MDL % MDL % MDL % MDL %

Monthly disposable income per capita 677 100.0 619 100.0 930 100.0 911 100.0 1 414 100.0 1 515 100.0

Income from employment 201 29.7 185 29.9 372 40.0 364 40.0 1 013 71.6 1 079 71.2

Income from self-employment in agriculture 161 23.8 116 18.7 132 14.2 95 10.4 44 3.1 44 2.9


33 4.9 64 10.3 100 10.8 92 10.1 68 4.8 58 3.8

Income from property 2 0.3 0 0.0 3 0.3 5 0.5 2 0.1 3 0.2

Social payments 92 13.6 105 17.0 51 5.5 57 6.3 69 4.9 61 4.0

pensions 75 11.1 85 13.7 31 3.3 34 3.7 43 3.0 35 2.3

child allowances 7 1.0 5 0.8 6 0.6 10 1.1 11 0.8 12 0.8

family compensations 3 0.4 5 0.8 4 0.4 4 0.4 1 0.1 2 0.1

Other incomes 189 27.9 149 24.1 272 29.2 298 32.7 218 15.4 271 17.9

remittances 163 24.1 116 18.7 231 24.8 257 28.2 120 8.5 231 15.2


Table A9: Monthly average per capita income by period and gender of household head


Male Female

Before crisis In crisis Before crisis In crisis

MDL % MDL % MDL % MDL %

Monthly disposable income per capita 943 100.0 924 100.0 939 100.0 927 100.0

Income from employment 466 49.4 445 48.2 332 35.4 355 38.3

Income from self-employment in agriculture 134 14.2 102 11.0 106 11.3 71 7.7

Income from self-employment in non-agriculture 86 9.1 97 10.5 66 7.0 44 4.7

Income from property 1 0.1 3 0.3 6 0.6 5 0.5

Social payments 58 6.2 63 6.8 75 8.0 81 8.7

pensions 38 4.0 40 4.3 55 5.9 59 6.4

child allowances 8 0.8 11 1.2 6 0.6 6 0.6

family compensations 3 0.3 4 0.4 3 0.3 4 0.4

Other incomes 197 20.9 214 23.2 355 37.8 371 40.0

remittances 155 16.4 182 19.7 301 32.1 314 33.9


81

Table A10: Distribution of population by quintiles of incomes by periods (%)

Quintiles Before crisis In crisisQ I Q II Q III Q IV Q V Q I Q II Q III Q IV Q V

Geo

grap

hi-

cal z

one

North 27.5 21.7 19.1 18.2 13.6 23.8 23.0 19.8 20.8 12.8Center 32.0 17.2 21.4 16.1 13.6 32.4 21.9 15.5 19.3 11.0South 30.6 23.9 15.9 21.2 8.5 33.7 21.4 19.6 14.9 10.6Chisinau 4.6 14.7 14.9 22.4 43.6 2.9 10.6 16.6 30.4 39.7

Total 24.4 19.2 18.3 19.2 19.0 23.5 19.4 17.7 21.4 18.1

Resi

-de

nce Urban 12.1 17.2 19.5 22.8 28.5 11.8 15.1 19.0 26.8 27.5

Rural 33.2 20.7 17.5 16.6 12.1 32.3 22.8 16.7 17.3 10.9Total 24.4 19.2 18.3 19.2 19.0 23.5 19.4 17.7 21.4 18.1

Num

ber

of

child

ren

up to

18

HH with 1 c. 18.4 16.8 18.2 21.8 25.0 18.0 17.9 16.8 23.9 23.5HH with 2 c. 25.8 21.5 19.9 17.9 15.0 25.7 19.5 19.0 20.5 15.5HH with 3 and more c. 49.1 23.1 13.7 10.6 3.6 39.5 25.9 17.3 14.0 3.5

Total 24.4 19.3 18.3 19.2 19.0 23.5 19.4 17.7 21.4 18.1

Stra

ta

Cities 8.1 13.1 16.7 19.5 42.6 4.0 12.5 16.9 28.0 38.7Towns 16.3 22.5 23.1 27.2 10.9 20.8 19.0 22.4 26.0 11.9Villages 33.2 20.7 17.5 16.6 12.1 32.3 22.8 16.7 17.3 10.9

Total 24.3 19.3 18.3 19.2 18.9 23.4 19.6 17.8 21.5 17.9

Soci

o-ec

onom

ic

stat

us o

f hou

se-

hold

hea

d 60

1 53.2 16.6 9.0 11.9 9.4 63.0 20.5 8.9 4.3 3.32 34.3 22.2 14.7 13.3 15.6 40.0 18.5 17.7 19.2 4.63 10.5 17.8 21.0 24.4 26.5 11.3 16.6 15.8 27.6 28.94 0.0 9.1 0.0 0.0 91.0 14.4 0.0 6.8 52.0 26.85 31.9 27.2 21.5 15.2 4.4 25.2 26.2 27.3 17.0 4.46 18.4 16.4 20.1 21.2 23.9 15.8 19.2 20.3 23.1 21.7

Total 24.3 19.3 18.3 19.2 18.9 23.5 19.4 17.7 21.4 18.1

Educ

ation

le

vel o

f ho

useh

old

head

61

1 8.1 9.3 16.1 27.5 39.1 6.0 9.2 14.3 29.6 41.02 23.6 20.9 18.1 19.8 17.8 23.8 19.2 18.7 21.7 16.73 38.7 21.1 21.2 11.4 7.7 34.8 26.2 17.0 15.1 7.04 40.4 25.8 5.7 13.0 15.2 16.7 33.4 50.0 0.0 0.0

Total 24.4 19.2 18.3 19.2 19.0 23.5 19.4 17.7 21.4 18.1

Age

of h

ouse

hold

he

ad

<30 years 19.5 12.5 25.0 16.0 27.1 18.0 19.2 14.6 24.4 23.830-39 y. 22.0 16.4 14.0 21.4 26.2 21.5 20.1 14.9 23.4 20.340-49 y. 23.4 19.8 17.7 21.0 18.1 25.2 17.8 15.5 23.5 18.150-59 y. 30.7 22.7 18.6 15.7 12.4 27.1 13.7 22.8 18.0 18.560-64 y. 25.3 18.4 21.4 26.3 8.7 26.7 22.9 28.0 10.1 12.3>65+ y. 30.2 28.0 23.8 14.2 4.1 21.8 31.0 29.7 14.9 2.7Total 24.4 19.2 18.3 19.2 19.0 21.8 17.0 16.1 25.6 19.5

Type

of

care

take

r Couple 22.7 15.4 18.8 20.6 22.7 23.7 18.1 16.0 22.4 20.0Lonely parent 25.2 29.5 16.8 14.4 14.3 19.9 22.3 16.2 24.5 17.3Wider family 25.7 20.9 18.2 18.9 16.4 23.8 20.1 19.5 20.0 16.6

Total 24.4 19.2 18.3 19.2 19.0 23.5 19.4 17.7 21.4 18.1

Sex

of H

H

head

Male 23.6 17.4 18.5 21.9 18.7 24.4 19.1 17.3 20.6 18.7Female 25.9 22.9 17.9 14.1 19.5 21.5 20.1 18.6 22.9 16.9Total 24.4 19.2 18.3 19.2 19.0 23.5 19.4 17.7 21.4 18.1

Mar

ital

Sta

tus

of

HH

hea

d

single 13.3 23.9 35.7 4.6 22.7 16.9 17.6 14.1 35.5 16.0married 22.7 17.5 17.7 21.1 21.3 22.8 17.7 17.6 21.4 20.5unofficially married 49.0 11.3 32.1 7.7 0.0 19.4 45.9 0.0 28.5 6.2

widowed 35.2 23.2 19.0 16.0 6.7 26.0 23.4 24.7 19.8 6.1divorced 22.7 30.1 17.6 10.4 19.3 28.7 23.1 13.3 19.3 15.8Total 24.4 19.2 18.3 19.2 19.0 23.5 19.4 17.7 21.4 18.1

Remittances as main source of income 18.2 13.8 18.1 16.1 34.0 14.1 18.0 20.3 26.5 21.1

Remittances as secondary source of income 10.2 19.7 19.8 25.9 24.4 8.9 12.8 18.4 22.3 37.8


60 Farmers – 1; Employees in agricultural sector – 2; Employees in non-agricultural sector – 3; Entrepreneurs – 4; Pen-sioner – 5; Other – 661 Higher – 1; Secondary – 2; Primary or incomplete secondary – 3; Without primary - 4

82

Table A11: Distribution of population by quintiles of consumption by periods (%)

Quintiles Before crisis In crisisQ I Q II Q III Q IV Q V Q I Q II Q III Q IV Q V

Geo

grap

hi-

cal z

one

North 23.9 23.9 23.8 16.3 12.2 23.3 23.9 22.3 18.3 12.3Center 28.2 22.7 17.0 18.4 13.9 27.9 27.1 18.6 15.2 11.3South 23.2 28.3 20.8 17.8 10.0 31.1 25.0 23.1 11.8 9.2Chisinau 6.5 11.4 15.5 29.4 37.3 2.4 6.2 14.4 35.3 41.8

Total 21.2 21.8 19.4 20.1 17.7 21.4 21.0 19.5 20.0 18.3

Resi

-de

nce Urban 11.0 18.5 18.6 25.3 26.7 9.8 12.3 19.1 28.0 30.9

Rural 28.4 24.2 19.9 16.4 11.2 30.2 27.5 19.9 13.8 8.7Total 21.2 21.8 19.4 20.1 17.7 21.4 21.0 19.5 20.0 18.3

Num

ber

of

child

ren

up to

18

HH with 1 c. 15.6 19.6 18.0 23.6 23.4 13.9 18.3 21.3 24.3 22.3HH with 2 c. 22.8 23.7 21.3 18.3 14.0 24.3 24.2 18.8 17.1 15.7HH with 3 and more c. 42.9 25.7 19.2 9.6 2.7 43.7 21.5 14.4 10.9 9.6

Total 21.2 21.8 19.4 20.1 17.7 21.4 21.0 19.5 20.0 18.3

Stra

ta

Cities 9.6 16.1 14.8 26.6 33.1 4.0 8.2 13.0 33.5 41.4Towns 12.9 21.7 23.1 24.0 18.4 16.2 17.8 27.8 21.6 16.8Villages 28.4 24.2 19.9 16.4 11.2 30.2 27.5 19.9 13.8 8.7

Total 21.2 21.9 19.4 20.2 17.5 21.2 21.1 19.7 20.0 18.2

Soci

o-ec

onom

ic

stat

us o

f hou

se-

hold

hea

d 62

1 32.9 27.7 19.1 10.9 9.5 43.7 29.0 17.2 7.7 2.62 39.9 17.6 14.4 19.8 8.4 38.8 28.9 11.2 9.3 11.93 12.6 18.6 19.2 26.2 23.5 12.2 17.5 19.2 24.9 26.34 0.0 0.0 0.0 33.0 67.1 0.0 6.8 6.1 53.5 33.75 29.3 29.1 23.8 12.6 5.3 32.8 24.7 18.5 18.0 6.26 15.6 18.2 18.6 20.8 26.9 12.8 18.1 23.5 22.0 23.7

Total 21.2 21.9 19.4 20.2 17.5 21.4 21.0 19.5 20.0 18.3

Educ

ation

le

vel o

f ho

useh

old

head

63

1 4.6 12.6 20.5 31.0 31.5 3.1 6.8 19.0 34.6 36.62 20.8 22.5 18.6 20.8 17.3 19.8 21.9 21.0 19.6 17.83 33.2 26.8 20.8 10.1 9.3 38.6 28.2 15.8 10.8 6.74 57.3 12.4 15.5 6.2 8.7 92.3 0.0 0.0 0.0 7.7

Total 21.2 21.8 19.4 20.1 17.7 21.4 21.0 19.5 20.0 18.3

Age

of h

ouse

hold

he

ad

<30 y. 16.1 16.4 15.4 24.4 27.8 13.8 12.2 24.2 22.4 27.630-39 y. 20.2 17.7 15.5 22.7 24.0 16.3 20.7 16.8 24.4 22.040-49 y. 17.7 22.5 24.2 19.8 15.9 23.0 20.7 19.8 20.3 16.450-59 y. 25.9 25.2 19.5 16.4 13.1 24.2 24.0 21.9 12.7 17.460-64 y. 27.7 28.2 19.3 18.7 6.4 19.3 28.3 22.8 21.8 7.9>65+ y. 30.9 30.8 20.7 13.5 4.2 38.7 25.2 17.9 13.9 4.4Total 21.2 21.8 19.4 20.1 17.7 21.4 21.0 19.5 20.0 18.3

Type

of

care

take

r Couple 19.8 20.0 17.5 22.4 20.3 20.0 17.6 21.8 19.1 21.5Lonely parent 19.6 21.3 27.0 13.8 18.3 12.1 22.3 11.3 29.0 25.5Wider family 22.7 23.6 19.8 19.2 14.8 24.4 23.7 19.0 19.0 14.0

Total 21.2 21.8 19.4 20.1 17.7 21.4 21.0 19.5 20.0 18.3

Sex

of H

H

head

Male 21.9 21.4 19.1 20.5 17.2 22.2 19.8 21.8 18.0 18.2Female 19.8 22.5 19.8 19.4 18.6 19.7 23.2 15.0 23.9 18.3Total 21.2 21.8 19.4 20.1 17.7 21.4 21.0 19.5 20.0 18.3

Mar

ital

Sta

tus

of

HH

hea

d

single 29.5 8.2 9.0 21.7 31.6 14.7 9.1 7.1 29.8 39.4married 19.7 20.1 19.3 21.7 19.4 20.5 19.2 21.0 20.3 19.2unofficially married 49.5 7.7 11.3 31.6 0.0 31.8 20.6 30.1 17.6 0.0

widowed 26.3 32.7 21.0 13.6 6.6 28.1 29.8 17.9 15.4 9.0divorced 21.9 22.6 20.8 15.8 19.1 17.8 29.2 6.7 21.8 24.6Total 21.2 21.8 19.4 20.1 17.7 21.4 21.0 19.5 20.0 18.3

Remittances as main source of income 8.6 12.5 18.4 22.9 37.8 12.1 16.6 27.2 20.6 23.7

Remittances as secondary source of income 8.1 15.8 29.6 27.5 19.0 8.3 20.7 18.5 24.9 27.7


62 Farmers – 1; Employees in agricultural sector – 2; Employees in non-agricultural sector – 3; Entrepreneurs – 4; Pen-sioner – 5; Other – 663 Higher – 1; Secondary – 2; Primary or incomplete secondary – 3; Without primary - 4

83

Table A12: Percentage change of income by geographical zone (%)

North Center South Chisinau

Income change


Income change


Income change


Income change


Disposable income per capita 2.6 100.0 -7.1 100.0 -5.5 100.0 1.4 100.0

Income from employment 6.1 31.0 -11.1 31.0 -5.4 34.7 8.2 65.6


-18.8 13.9 -21.9 17.5 -34.7 13.0 -61.7 0.4


-29.4 5.0 22.0 10.2 -16.6 3.9 40.6 8.4

Income from property 148.8 0.4 29.8 0.2 140.3 0.4 -39.8 0.4

Social payments 3.6 18.6 -3.3 15.7 11.4 18.6 -2.4 9.8

pensions 2.3 15.6 0.1 13.0 14.5 15.8 -4.7 8.3

child allowances 28.7 0.5 -6.7 0.4 21.0 0.4 102.4 0.7

nominative compensations 15.9 1.0 16.9 0.7 38.5 1.0 11.7 0.3

Other incomes 20.1 31.2 -0.8 25.3 6.0 29.4 -24.1 15.3

remittances 22.1 25.7 1.0 21.8 8.2 25.3 20.9 10.8

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data; Obs: In 2007 Oct prices;

Table A13: Percentage change of income by HH size (%)

1 - 2 3 4 5 and +

Income change


Income change


Income change


Income change


Disposable income per capita -0.4 100.0 -5.3 100.0 7.6 100.0 -1.7 100.0

Income from employment 7.8 34.0 2.0 52.2 6.1 51.5 -12.3 39.1


-37.3 7.6 -26.3 8.1 -7.9 11.3 -20.9 15.6


-6.0 4.7 21.7 6.5 9.4 9.0 11.0 10.8

Income from property 25.5 0.5 32.8 0.3 -39.0 0.3 19.8 0.3

Social payments 8.8 26.6 7.9 8.7 -11.7 5.7 16.0 12.8

pensions 10.7 24.4 13.7 6.7 -10.9 3.9 5.8 8.6

child allowances -30.3 0.0 43.4 0.5 -13.4 0.5 68.7 1.9

nominative compensations 22.5 0.9 4.1 0.5 23.8 0.4 63.5 0.8

Other incomes -1.0 26.6 -18.8 24.3 30.8 22.2 30.7 21.3

remittances 11.7 19.4 -12.2 20.6 61.3 19.5 36.7 18.9

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data; Obs: In 2007 Oct prices;

84

Table A14: Income change by sex of household head – before and in crisis (%)

Male headed household Female headed household

Income change

Income structure in

crisis

Income change

Income structure in

crisis

Disposable income per capita -3.1 100.0 1.2 100.0

Income from employment -1.6 46.0 9.6 38.5

Income from self-employment in agriculture -21.4 11.6 -34.8 6.7

Income from self-employment in non-agriculture 21.1 8.7 -23.3 4.3

Income from property -21.8 0.3 54.5 0.5

Social payments -6.0 13.1 15.9 18.3

pensions -6.1 10.8 16.8 15.9

child allowances 49.9 0.7 10.1 0.3

nominative compensations 19.4 0.6 23.5 0.8

Other incomes 0.7 20.2 0.1 31.7

remittances 16.7 16.6 10.6 25.5


Table A15: Income change household heads occupational status – before and in crisis (%)

FarmersEmployees in agricultural

sector

Employees in non-

agricultural sector

Entrepreneurs Pensioners Other

111 2 1 2 1 2 1 2 1 2 1 2

Disposable income per capita

-28.4 100.0 -7.7 100.0 0.8 100.0 -4.4 100.0 -0.6 100.0 -0.6 100.0


-10.7 22.0 -2.0 50.7 4.6 74.4 9.2 18.2 18.0 19.1 27.6 9.1


-6.1 45.2 -22.8 22.5 -33.2 3.8 -2.5 3.8 -27.7 12.8 -33.4 5.5


-39.9 1.6 -45.1 2.0 19.1 2.0 7.5 72.5 -14.4 2.3 -15.6 24.2

Income from property -67.7 0.2 0.0 39.1 0.2 145.9 0.3 1.1 0.9

Social payments -9.6 9.5 -2.0 6.0 5.4 5.7 69.7 4.5 9.3 49.7 -11.6 2.9

pensions -17.1 6.0 -3.5 4.1 6.1 3.9 71.5 3.6 10.8 45.8 -26.8 1.1

child allowances -9.0 0.8 42.7 0.7 70.4 0.6 -25.3 0.2 29.2 0.7

nominative compensations

66.8 1.0 -7.5 0.4 0.8 0.3 415.0 0.4 31.7 1.9 -2.7 0.2

Other incomes -59.5 21.6 6.3 18.9 -8.3 13.9 -77.2 1.0 -14.5 15.7 9.5 57.5

remittances -64.4 17.2 1.8 15.3 19.3 10.7 -82.9 0.4 -9.1 9.4 22.9 51.8

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data. Obs: In 2007 Oct prices.

64 1 – Income change; 2 – Income structure in crisis period.

85

Table A16: Per capita expenditures by Geographical zone

North Center South Chisinau

Ex-penditure

change (%)

Ex-penditure structure in crisis period

Ex-penditure

change (%)


Ex-penditure

change (%)


Ex-penditure

change (%)


Consumption expenditure per capita

-2.8 100,0 -13.3 100,0 -12.0 100,0 0.6 100,0

food -14.2 41,0 -17.4 43,2 -17.1 45,4 -6.4 36,8

beverages -17.0 1,5 -38.2 2,5 -32.8 2,3 -7.8 1,4

clothes and foot wear -0.7 14,1 -9.8 12,6 -23.3 10,1 6.0 13,2

dwelling maintenance 2.5 15,6 -2.1 15,4 5.5 16,2 30.4 14,0

dwelling equipment -1.5 4,3 -15.0 4,1 -23.4 3,6 -8.9 3,1

health 30.2 7,6 -5.9 6,9 17.1 7,7 9.2 6,4

transport 9.2 3,4 -20.8 4,2 -12.6 3,4 -14.6 5,9

communication 14.0 5,1 0.7 4,7 3.4 5,6 12.3 5,7

entertainment -8.7 1,3 -5.0 1,6 -23.9 0,9 -14.9 2,7

education 928.0 1,0 4.7 0,3 67.7 0,3 47.7 0,7

Hotels, restaurants 40.8 1,1 -40.0 0,9 -9.8 1,1 -13.4 4,6

others 9.4 4,0 -2.5 3,6 -10.5 3,4 6.3 5,3


Figure A1: Correlation plot between per capita consumption change (% before and in crisis) and complete SADI index

Source: NBS; HBS IV 2007; I 2008, IV 2008 and I 2009; self processed data & SADI Index 2007

86

Figure A2: Correlation plot between per capita consumption change (% before and in crisis) and Income deprivation component of SADI index


Figure A3: Correlation plot between per capita consumption change (% before and in crisis) and Demographic deprivation component of SADI index


87

Figure A4: Correlation plot between per capita consumption change (% before and in crisis) and Economic deprivation component of SADI index


Figure A5: Correlation plot between per capita consumption change (% before and in crisis) and Health deprivation component of SADI index


88





89

Table A17: SADI index for 3 person HH in Central and South regions, rural area

RayonN HH with 3

personsPercent

Index of multiple deprivation - SADI, - rayon average

60 R-UL NISPORENI 8,664 2.1 206.81

80 R-UL STRASENI 51,331 12.5 242.48

89 R-UL TELENESTI 16,128 3.9 253.07

21 R-UL CANTEMIR 20,732 5.1 256.55

53 R-UL HINCESTI 34,666 8.5 261.42

25 R-UL CALARASI 27,476 6.7 266.10

67 R-UL REZINA 6,140 1.5 288.75

92 R-UL UNGHENI 22,927 5.6 302.45

64 R-UL ORHEI 41,423 10.1 306.32

55 R-UL IALOVENI 29,368 7.2 389.19

57 R-UL LEOVA 6,467 1.6 424.68

17 R-UL CAHUL 18,600 4.5 466.80

31 R-UL CRIULENI 2,867 0.7 471.47

29 R-UL CIMISLIA 10,060 2.5 511.80

27 R-UL CAUSENI 13,689 3.3 527.98

38 R-UL DUBASARI 14,020 3.4 535.87

10 R-UL ANENII NOI 16,427 4.0 605.17

85 R-UL STEFAN VODA 6,371 1.6 613.84

12 R-UL BASARABEASCA 10,296 2.5 653.72

96 GAGAUZIA 35,864 8.8 700.54

87 R-UL TARACLIA 16,155 3.9 757.33

Total 409,670 100.0 398.98

Source: SADI Index data base.

Obs.: Threshold 50% of mean SADI index and rayons which fall below this threshold in at least three out of four categories highlighted.

90

Table A18: SADI index for > 4 person HH in Central and South regions, rural area

RayonN HH > 4 persons

PercentIndex of multiple deprivation -

SADI, - rayon average

60 R-UL NISPORENI 17,633 2.6 206.81

80 R-UL STRASENI 42,462 6.4 242.48

89 R-UL TELENESTI 63,466 9.5 253.07

21 R-UL CANTEMIR 42,656 6.4 256.55

53 R-UL HINCESTI 43,113 6.5 261.42

25 R-UL CALARASI 36,713 5.5 266.10

67 R-UL REZINA 2,316 0.3 288.75

92 R-UL UNGHENI 35,032 5.3 302.45

64 R-UL ORHEI 41,669 6.3 306.32

55 R-UL IALOVENI 41,896 6.3 389.19

57 R-UL LEOVA 30,623 4.6 424.68

17 R-UL CAHUL 73,804 11.1 466.80

31 R-UL CRIULENI 22,923 3.4 471.47

29 R-UL CIMISLIA 41,165 6.2 511.80

27 R-UL CAUSENI 19,276 2.9 527.98

38 R-UL DUBASARI 5,960 0.9 535.87

10 R-UL ANENII NOI 18,793 2.8 605.17

85 R-UL STEFAN VODA 28,677 4.3 613.84


96 GAGAUZIA 39,165 5.9 700.54

87 R-UL TARACLIA 10,402 1.6 757.33

Total 666,387 100.0 397.61


Obs.: Threshold 50% of mean SADI index and rayons which fall below this threshold in at least three out of four categories highlighted.

91

Table A19: SADI index for HH with children up to 16 in Central and South regions, rural area

RayonN HH with children up

to 16Percent


60 R-UL NISPORENI 14,887 2.8 206.81

80 R-UL STRASENI 43,709 8.2 242.48

89 R-UL TELENESTI 57,428 10.7 253.07

21 R-UL CANTEMIR 12,985 2.4 256.55

53 R-UL HINCESTI 26,987 5.0 261.42

25 R-UL CALARASI 22,539 4.2 266.10

67 R-UL REZINA 3,864 0.7 288.75

92 R-UL UNGHENI 33,039 6.2 302.45

64 R-UL ORHEI 52,643 9.8 306.32

55 R-UL IALOVENI 36,964 6.9 389.19

57 R-UL LEOVA 15,872 3.0 424.68

17 R-UL CAHUL 52,520 9.8 466.80

31 R-UL CRIULENI 20,726 3.9 471.47

29 R-UL CIMISLIA 15,197 2.8 511.80

27 R-UL CAUSENI 11,236 2.1 527.98

38 R-UL DUBASARI 8,735 1.6 535.87

10 R-UL ANENII NOI 25,369 4.7 605.17

85 R-UL STEFAN VODA 14,021 2.6 613.84


96 GAGAUZIA 28,550 5.3 700.54

87 R-UL TARACLIA 26,919 5.0 757.33

Total 535,419 100.0 405.89


Obs.: Threshold 50% and 66% of mean SADI index and rayons which fall below this threshold in at least three out of four categories highlighted.

92

Table A20: SADI index for HH with 1 child up to 16 in Central and South regions, rural area

RayonN HH with 1

child up to 16Percent


60 R-UL NISPORENI 36,730 2.9 206.81

80 R-UL STRASENI 93,140 7.5 242.48

89 R-UL TELENESTI 116,498 9.4 253.07

21 R-UL CANTEMIR 53,178 4.3 256.55

53 R-UL HINCESTI 91,649 7.4 261.42

25 R-UL CALARASI 69,574 5.6 266.10

67 R-UL REZINA 9,055 0.7 288.75

92 R-UL UNGHENI 70,335 5.6 302.45

64 R-UL ORHEI 105,641 8.5 306.32

55 R-UL IALOVENI 80,699 6.5 389.19

57 R-UL LEOVA 39,651 3.2 424.68

17 R-UL CAHUL 109,661 8.8 466.80

31 R-UL CRIULENI 40,809 3.3 471.47

29 R-UL CIMISLIA 56,597 4.5 511.80

27 R-UL CAUSENI 42,861 3.4 527.98

38 R-UL DUBASARI 18,693 1.5 535.87

10 R-UL ANENII NOI 54,385 4.4 605.17

85 R-UL STEFAN VODA 42,233 3.4 613.84


96 GAGAUZIA 50,920 4.1 700.54

87 R-UL TARACLIA 39,420 3.2 757.33

Total 1,245,466 100.0 395.19


Obs.: Threshold 50% and 66% of mean SADI index and rayons which fall below this threshold in at least three out of four categories highlighted.

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