2. a Profile of Rural Poverty
World Bank National Commission for Statistics Public Disclosure Authorized
Public Disclosure Authorized From Rural Poverty to Rural
Public Disclosure Authorized Development Public Disclosure Authorized Coordinated by
Constantin Chircă, Vice-President, National Commission for Statistics and Emil Daniel Teşliuc, Economist, World Bank
1999 Report Coordinated by:
Constantin Chircă Vice President, National Commission for Statistics, Romania Emil Daniel Teşliuc Economist, World Bank Field Office, Romania
Authors:
Mariana Câmpeanu National Commission for Statistics Doina Gheorghe National Commission for Statistics Radu Halus National Commission for Statistics Filofteia Panduru National Commission for Statistics Marius Augustin Pop National Commission for Statistics Dumitru Sandu Bucharest University Emil Daniel Teşliuc World Bank Field Office, Romania
With the contribution of Ms. Marina Liana, National Commission for Statistics
The authors would like to acknowledge their indebtedness to Mr. Henry Gordon (World Bank), team leader of the World Bank rural development project, for the suggestions made which substantially improved the content of the report, and for the financial support without which the printing of the report would have not been possible.
The team is thanking Mr. Victor Dinculescu, President of the National Commission for Statistics, for the very useful recommendations and for the support given to the whole research team in the production of the report.
The content of the paper was substantially improved thanks to the recommendations received from Messrs. Lucian Croitoru (National Bank of Romania), Farid Dhanji (World Bank), Valentin Lazea (National Bank of Romania), Lucian Luca (Research Institute for Agricultural Economics), Maria Molnar (National Institute for Economic Research), Gheorghe Oprescu (Competition Council), Lucian Pop (Bucharest University), Cornel Tarhoacă (Academy for Economic Studies) and Luiza Toma (Research Institute for Agricultural Economics). To all of them, the authors wish to express their warm thanks.
The team of authors wants to thanks to all the participants at the seminars organized in July 7th and 9th, 1999, by the National Commission for Statistics and the World Bank in Bucharest and Vaslui. Through their comments, suggestions and questions, the participants at the seminars helped the authors to improve the content of the report.
The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the World Bank, to its affiliated organizations, or National Commission for Statistics. SUMMARY
Introduction...... i
1. Rural Development Resources...... 1
1.1.The Rural Human Capital ...... 5
1.1.1. Population...... 5
1.1.2. Labor ...... 10
1.2. Physical Resources of Rural Households ...... 16
1.2.1. Land ...... 16
1.2.2. Livestock ...... 17
1.2.3. Productive Equipment ...... 18
1.3. Infrastructure in Rural Communities ...... 18
1.3.1. Physical Infrastructure in Rural Areas ...... 24
1.3.2. Human Capital Improvement Infrastructure...... 24
2. A Profile of Rural Poverty ...... 26
2.1. Poverty Measurement ...... 27
2.2. Rural Poverty and Demographic Characteristics of the Household...... 28
2.3. Rural Poverty and Household Resources...... 29
2.3.1. Rural Pensioners ...... 32
2.3.2. Rural Employees ...... 33
2.3.3. Farmers, Access to Land and the Role of Agriculture ...... 36
2.4. Robustness of the “Consumption” Poverty Measure ...... 42
2.5. Regional Dimensions of Rural Poverty...... 43
2.6. Summary and Conclusions ...... 44
3. Living Standards And Development Resources...... 47
3.1. Factors Influencing Households Consumption ...... 47
3.2. Key Predictors Of Household Consumption...... 52 3.3. Predicting The Development Level Of Rural Communes ...... 54
3.4. Summary And Conclusions...... 59
4. Findings and Policy Recommendations...... 61
4.1. From Agricultural Policies To Rural Development Policies ...... 61
4.2. Characteristics Of Rural Poverty ...... 65
4.3. From Rural Poverty To Rural Development ...... 68
4.3.1. Restructuring The Small-Farmer Sector ...... 69
4.3.2. Diversification Of The Rural Economy ...... 71
4.3.3. Development Of Human Capital In Rural Area ...... 73
4.4. The Regional Dimension Of Rural Development ...... 73
Annex 1. Thematic Maps ...... 74
Annex 2. Statistical Tables...... 85
Annex 3. Methodology Used in the Measurement of Poverty...... 102
Annex 4.Communes Belonging to the Poorest Quintile ...... 116 Introduction And Executive Summary
In 1999, four out of ten rural residents are poor. There are talks about rural development in Bucharest, Brussels and Washington. And yet, the fact is the widening gap between the Romania’s rural poor and the wishes of rulers, international donors or development agencies. How long is the way from rural poverty to rural development? What can we do to make it shorter? This is the major question that the authors of the paper have asked.
While there are people who would do something, there are others who would rather step back and think. The authors of this paper belong to the latter category, but they wrote the paper hoping that the people belonging to the former category would read it. So that they may do something about it – and do it right. Doers, those who «do something because something must be done», are strong- willed persons who do not think twice before acting. When things are simple, this is a behavior that may yield results. Unfortunately, the state of underdevelopment of rural Romania is a fact that cannot be changed simply by a desire to do good. We have witnessed many well-intended actions to take the Romanian countryside out of poverty failing miserably. Without exception, they have all been cases of willful action.
The authors of this paper do not think that «everybody knows what agriculture, or rural development is about». They tried to understand rural developments in recent years. They used rich sources of information on rural living standards, on the root causes of poverty and on what could take rural Romania out of poverty. They used rich micro datasets to give weight to their analysis. First they gave a description of rural area. Then, they asked themselves why things are the way they are: what disease does the Romanian rural area suffer from, what are the causes of rural poverty? Finally, they asked the question of what should be done to change poverty today for development tomorrow. And what should be done to bring that tomorrow nearer to us.
The authors’ believe that Romanian countryside can be taken out from its current state of underdevelopment only if market mechanisms become stronger in rural area. The failures and costs of etatist policies are documented in the paper, for fear that recent history may repeat itself. Furthermore, the agricultural reform measures initiated in 1997 are shown to be just a beginning, hardly enough to put the Romanian villages in orbit for rural development. However, the reform had the merit of ending the agricultural policy bias to the state sector and against the small farmer sector. It also diminished the macroeconomic instability sources traced to state agriculture. And yet, while creating a level playing field for all rural entrepreneurs, the reforms that began in 1997 could not break the vicious circle of poverty in rural area. Rural development concerns were secondary in the reform package early in the process. They seem to be a top priority now, at the eve of a new millennium.
The paper points to the vicious circle that developed in the rural area, where poverty breeds poverty. Most farm households are poor, and poverty has taken them away from modern, forward- looking economy to primitive, subsistence farming. This obsolete mode of production that leads nowhere cannot but perpetuate the poverty of those employed in this activity. It is difficult to take the rural poor out of poverty, first of all because there are many of them, there are 4.2 million out of 10 million rural inhabitants. Because of poverty, rural markets are weak: there are few buyers of farm inputs required by modern farming, and there are few sellers of farm output. Rural Romania does not make the transition to a market economy: it remains frozen in a natural, subsistence economy. And because there are few buyers and sellers in rural area, the businesses in industries, trade or services are likewise.
I The farm-household sector has yet to recover from the dramatic structural effects of the land reform. Land ownership is highly scattered and there is a mismatch between access to land, labor and capital at household level. While old farmers do not have access to labor, young farmers do not have access to land. Besides, productive equipment is scarce, obsolete and inadequate for small-scale farming. And input and output marketing channels as well as extension and credit services are not tailored to small farmer needs.
A rural development strategy is required to address these problems. First of all, part of the land owned by old farmers should be transferred, by various market mechanisms such as land lease, sale or sharecropping, to younger, more efficient farmers. Second, part of the farmers today should find non-agricultural jobs in rural area, or retire. This would improve the labor-to-land ratio and would increase the marginal productivity of farm labor, so that agriculture could generate higher incomes and provide for decent living standards for the peasantry.
Rural development policies are needed to break the vicious circle of poverty, by providing incentives for the villages to come out of isolation, as well as regulations for these policies to be pursued, institutions to implement them and money. Concerted action needs to be taken at every decision-making level.
With the diagnosis provided in the paper as a basis, we suggest a rural development strategy the priorities of which are as follows: 1) Rapid structural adjustment of the small farmer sector by: a) Measures that stimulate the land market and agricultural land consolidation; b) Measures that stimulate farmer partnerships for input procurement and output marketing; c) Promotion of activities in the nature of public goods for the benefit of household farmers. 2) Diversification of rural employment by providing support to non-agricultural occupations. 3) Development of human capital.
Many of these measures call for adequate policies, cooperation between public institutions, between central and local authorities, and between them and civil society along with the private sector. Others require finance, hard to find at a time of economic decline when both the central and local budgets are under increasing constraints. Fortunately, scarce domestic resources can be complemented by foreign loans or grants. Rural development and poverty alleviation are major goals for the attainment of which the international community is prepared to provide important financial resources in grants or loans. The European Union has made significant allocations for this purpose under its SAPARD program. Also the World Bank is ready to support the implementation of a rural development strategy with attractive long-term loans. Wisely used, these funds can take rural Romania out of its current isolation and poverty. We hope this paper contributes to the effort.
II 1. Rural Development Resources
Administratively, the rural area is comprised of 2686 communes incorporating 13 thousand villages. It is very different from urban area by economic activities, occupations and factors of production. It also is very heterogeneous, with tiny human settlements and larger communities, densely populated areas and isolated villages and hamlets, rich communities and poor communities. However, the rural area is particularly important to Romania - in both economic and social terms: it accounts for 45% of the country’s population, 47% of housing and 46% of the living space. In this respect, Romania is unlike either its neighbors or EU countries where only 23% of the total are rural population. In Romania, 89% of the country’s surface area and 91% of its farmland area is under rural community administration. With 48 people per sq. km., the rural area is more sparsely populated than the urban area (where the figure is 480).
The rural area lags behind Table 1.1. Differences In Occupational Status By Area Of Residence the urban one - socially and % of total, by area % of total, by category economically. Primary Occupational status Urban Rural Urban Rural productive activities prevail Employee 91.0 32.7 72.5 27.5 in rural area: farming, Employer 2.3 0.4 85.8 14.2 Self-employed 4.7 34.7 11.3 88.7 forestry and fishing. By Family worker 1.9 31.5 5.5 94.5 contrast, secondary and Member of agricultural company or 0.1 0.7 10.1 89.9 tertiary production – non-ag. cooperative manufacturing industries, Total 100.0 100.0 48.7 51.3 trade and other services are Source: CNS - Labor Force Participation Survey, 1997 dominant in urban area. Food processing and extraction industry are the most common industrial activity in rural area. This difference in economic activities also extends to occupations: most of the economically active population in rural area are self-employed farmers, unlike in urban area where employees cut the biggest share. It should, however, be said that while entrepreneurship measured by the proportion of employers in total economically active population is low in both areas, it is almost nil in the rural area, where less than one-fifth of all Romanian employers are located. These differences are apparent in Table 1.1. Table 1.2. Education Level Of Working Population By Area, 1997 % of total by area % of line item total Differences in occupational Urban Rural Urban Rural status are, in their turn, linked to Total 100.0 100.0 48.7 51.3 different educational No formal schooling 0.3 3.3 8.0 92.0 achievements and skills in the Primary school 2.3 24.3 8.4 91.6 two areas. The education level Lower secondary school 10.3 31.2 23.7 76.3 in rural areas is lower than in Lower high school 5.1 8.2 37.0 63.0 urban areas (see Table 1.2.). The Vocational school 24.7 17.4 54.7 42.6 working population with no Upper high school 34.0 12.8 71.7 28.3 formal schooling or with just Specialty post-high school 8.0 1.3 85.3 14.7 primary education makes up Long-course university 6.4 0.8 87.9 12.1 27.6% in rural area, which Short-course university 8.9 0.7 92.9 7.1 contrasts with a low 2.6% in Source: CNS – Household Labor Survey - AMIGO,1997 urban area. On the other hand,
1 higher school graduates are concentrated in the urban area where 90% of their total live.
Both economic activities and occupations are basically determined by resources in the two areas. The rural area makes intensive use of primary resources: land, forests and the subsoil. By land ownership, rural residents own some 80% of all farmland and operate an even higher percentage of agricultural landholdings. Livestock ownership is almost 100% in rural area. By contrast, rural ownership of production equipment, industrial construction, and infrastructure is far below the urban level.
The Romanian rural area is known to lag behind the urban area in the endowment with infrastructure and modern amenities (see Table 1.3. Household Welfare By Area Of Residence, 1997 Table 1.3). While such M.U. Rural Urban Rural/Urban indicators as the number of * Household Consumption rooms or living space per Total household consumption ‘000 ROL 97.4 109.3 0.89 capita do not differ Jan. 1995 significantly between the two Share of food consumption % 66.1 60.3 1.10 Housing Characteristics: Percent Of Dwellings With … areas, modern conveniences Made of concrete, bricks, or BCA % 45.4 91.1 0.50 are scarce in rural dwellings. Central heating unit, or gas pipeline % 8.5 81.5 0.10 Less than half of rural Water supply inside the building % 12.6 86.6 0.15 dwellings are made from time- With sewage system % 12.8 85.9 0.15 resistant building materials, Bathroom inside the building % 10.3 81.0 0.13 compared to 90 percent of the Toilet inside the building % 6.6 80.8 0.08 urban ones. Heating, sewage, Mean living space per person sq.m./pers. 15.5 14.7 1.05 or in-house bathroom seems Ownership Of Durable: Share Of Households Having … are found in more than 80 Gas cylinder cook stove % 60.0 90.6 0.66 percent of urban dwellings, but Refrigerator % 52.1 89.3 0.58 only 6 to 12 percent of the Washing machine % 24.9 60.4 0.41 rural dwellings have such TV set % 77.1 101.0 0.76 amenities. Rural households Passenger car % 11.3 25.6 0.44 Bedroom furniture % 50.3 79.3 0.63 also own fewer durables. The Source: LSMS (Living Standard Measurement Survey), 1997 household endowment with *) monthly average per equivalent adult housing or durable is consistent with the level of welfare, proxied by current consumption. In 1997, the mean consumption per adult equivalent was lower by 11 percent in rural areas than in urban areas. In rural areas, the pattern of consumption is specific for poorer areas: the share of household budget devoted to food is 66 percent in rural, compared to only 60 percent in urban areas.
There are similar differences in access to physical and social infrastructure between rural and urban area (see Table 1.4). The share of rural area in the total drinking water and gas distribution network is smaller than its share of total population. The shortfall is particularly wide for the sewage system: only as little as 6% of its total length is in rural area. As concerns social services, with the exception of classrooms, the rural area suffers from a chronic shortfall of health care infrastructure and personnel. The disparity is even wider in terms of quality, and for secondary – lower and upper – education and secondary health care services. Access to communications, to telephone services in particular, is four times more reduced in rural area.
2 The lower level of household Table 1.4. Indicators Of Physical And Social Infrastructure resources, social public services and Differences: Rural Vs. Urban Areas infrastructure in rural areas – both in Rural Urban terms of size and quality—, Population total – as of 07.01.1997 45% 55% correlates, as expected, with higher Physical Infrastructure - % By Rural/Urban Area mortality or infant mortality, indirect Length of drinking water distribution network – km 38% 62% measures of living standard. Length of gas distribution network –km 31% 69% Length of sewage system – km 6% 94% Poorer infrastructure or the Social Infrastructure – In 1000 People School units 2.1 0.7 prevalence of primary sector Teaching staff 10.3 16.8 activities in rural as compared to Classrooms and school laboratories 5.6 5.4 urban area is widespread in the Hospital and dispensary beds 2.0 14.7 world. This urban vs. rural disparity Medical staff (physicians and nurses) 2.5 12.1 is, perhaps, a rule of the economic Communications – In 1000 People growth. The disparities persist even Radio and local radio subscribers 147.2 231.5 in developed countries such as TV subscribers 116.4 233.5 European Union (EU), in spite of Individual telephone units 40.8 161.3 generous EU member-financed Crude Birth and Crude Death Rate regional and rural development Crude Birth Rate, per 1000 people 12.0 8.8 policies. Crude Death Rate, per 1000 people 16.5 9.5 Infant Mortality Rate, per 1000 live births 25.6 18.5 On the other hand, the rural area is Source: Territorial / Administrative Statistics 1996, NCS very heterogeneous. Average figures, used to illustrate the rural-urban disparity, tell nothing about this heterogeneity that has its roots in the wide diversity of economic, geographic and socio-cultural conditions in rural area. There are large rural communities comparable in size with towns, such as Voluntari in Ilfov county with a population of 27 thousand inhabitants, and small communities with a little over Table 1.5. Differences Across Romania’s Rural Communities one hundred inhabitants, such as Maximum Minimum Brebu Nou in Caras-Severin county Population, total – as of Jul-1st 27006 119 (Table 1.5). Furthermore, the Surface area, total – ha 80452 1098 landholdings managed by a Farmland area by use – ha 36611 150 community may vary within wide Housing stock available – total – units 7762 40 limits: Murighiol commune in Physical infrastructure Constanta county holds over 80 Length of drinking water distribution network - km. 133 0 Length of sewage system network– km 19.3 0 thousand hectares of land which Length of gas distribution pipelines -km. 75.9 0 contrasts with the one thousand Social infrastructure hectares held by Doicesti commune School units – total – numbers 27 1 in Dambovita county. And this wide Teaching staff, total – numbers 239 1 diversity of resources and living Physicians – numbers 68 1 standards may grow wider if we take Communications our analysis further down to villages. Telephone, telegraph and post offices - total – numbers 14 1 Individual telephone subscribers – numbers 1498 1 This wide diversity of resources in Source: Territorial / Administrative Statistics, NCS, 1996 rural area that translates into a diversity of living conditions and conveniences in Romanian villages
3 will be dealt with in the next chapters. Thematic maps will be used herein to illustrate the key factors – the resources – with the strongest impact on rural welfare. However, it should be kept in mind that the analysis in this paper does not go beyond the smallest local administrative unit – the commune, or “comuna” in Romanian – for which comparable statistics are available. Recent studiesi have indicated significant differences in welfare and access to infrastructure within one and the same community: as a rule, there is better access to infrastructure in the community seat than in the rest of its constituent villages.
This paper tests the following assumption: rural-urban differences in living standards and the wide diversity of rural living standards are largely accounted for by variations in the endowment with economic resources or inputs – work force, land, livestock, physical assets, physical and social infrastructure – in terms of quantity and qualityii. In this chapter, we will document the size and quality of the resources available in the rural areas, while pointing to their regional heterogeneity.
4 1.1. The Rural Human Capital
1.1.1. Population
We will analyze herein the evolution of rural population and the changes in its structure by age, sex, education or community size. Commune-level maps would help illustrate the geographic diversity of the population dynamics, migration and current mean education level. This section will be followed by a more detailed analysis of rural work force.
Rural population is a potential resource of great importance to village development. The data on the size and quality of human resources now and in future are decisive for rural development policy- making. Population size and dynamics, as well as its structure – across the rural area and by community - are such data of interest to policy-makers. The economically active population gives us an idea of the size of labor force of a community, whereas the inactive population tells us something about the population that is in need of assistance – be provided either by social security, social assistance or by the households themselves. The age structure of the population supplies information about labor, the size of the population past its productive age, but also of the young population that will replace the currently working population in due time. Population structure, dependency ratio and other similar indicators supply such information. Rural population dynamics tells us what the work force of rural communities can be expected in future, assuming the developments during the period of time identified as relevant for forecasts will persist. In addition, quality indicators such as the education level of the population of a given community help estimate the human resources of that community.
At 1st of July 1998, the rural population which totaled 10,139 thousand lived in 2686 communes comprising some 13 thousand villages. The average population of a commune was put at 3775, with that of a village at 780. However, the smallest commune had just 119 residents, the largest – over 27 thousand. A typical commune, between the first and third quartile, averaged between 2400 and 4800 residents.
Table 1.6. Dynamics Of The Rural Population Rural population 1977 1980 1985 1990 1995 1998 has dropped continuously during Population, thousand 12,164 12,030 11,355 10,598 10,234 10,139 the past twenty Population dynamics, 1977 = 100% 100.0 98.9 93.3 87.1 84.0 83.4 Rural population , % of total 56.4 54.2 50.0 45.7 45.1 45.0 years, as shown in Source: National Commission for Statistics Table 1.6. During Note: data at the beginning of the year 1977-1998, Romanian villages have lost about one sixth of their population: from 12 million twenty years ago, the rural population is currently put at some 10 million. That happened against the background of an all-country population rise until 1992, which was followed by a slight decrease. It was the outcome of the industrialization policies pursued by the socialist regime and persisted until 1997 as a result of the inertia characteristic of demographic phenomena. Overall, the share of the rural population has declined from 56.4% in 1977 to 45% now (1998).
5 There are demographic and Table 1.7. Impact of the Demographic Factors On Rural Population administrative factors behind Per 1000 people 1990 1991 1992 1993 1994 1995 1996 1997 the rural population decline. In Rural migration balance 47.3 -9.8 -7.2 -4.6 -3.1 -1.2 -0.4 1.2 the first place, it was due to the Rural natural population growth 0.9 -1.0 -1.9 -2.2 -2.4 -3.1 -4.5 -3.5 combined effect of migration – Rural population decline -9.7 -18.5 -3.2 -6.6 -7.1 -3.3 -4.9 -2.0 particularly of the young and better-educated people – to urban areas and a negative natural rise after 1990. Table 1.7 illustrates this. Secondly, in the past twenty years (1977-97) twenty large rural communities had their status changed to town, with their population being currently recorded as urban.
Table 1.8. Structure Of Migratory Flows By Area The most important determinant 1990 1991 1992 1993 1994 1995 1996 1997 of the decline of the rural Migration – thou. persons 786.7 262.9 293.2 240.2 266.7 289.5 292.9 302.6 population was migration, a Rural-rural, % 8.5 19.4 22.8 25.0 25.5 28.0 24.5 25.6 complex demographic, social Rural-urban, % 69.8 50.3 39.2 35.0 30.5 25.1 24.7 22.6 and economic phenomenon that Urban-rural, % 3.5 10.1 13.7 14.6 18.4 20.8 23.4 26.8 was behind the change in the Urban-urban, % 18.2 20.2 24.3 25.4 25.6 26.1 27.4 25.0 numbers and structure of the Rural-urban balance -66.3 -40.2 -25.5 -20.4 -12.1 -4.3 -1.3 +4.2 rural population. Of the migratory flows by areaiii – rural-rural, rural-urban, urban-rural and urban-urban – the most significant by its size until 1994 was the rural-urban population movement (see Table 1.8). The village-to-city migratory flow was the most significant until 1990. Early in 1990 when a legal ban on taking up residence in large cities was lifted, outmigration to city increased sharply. Beginning with 1991 which brought with it the land reform and industrial restructuring, rural outmigration declined with every passing year, as the urban population flow to rural area was growing. By 1997 the trend was reversed and the rural-urban migration balance turned positive – indicating an increase of the population which settled down in rural area through the combined effect of migratory flows – for the first time in thirty years.
In recent years migration within one and the same area has risen (to about 50%) against the background of a drop in village-to-city migration. Overall, however, migratory flows are fairly constant, indicating a constant population, hence work force, mobility.
The balance of rural migration is not the same across counties. While in 1991 the rural migration balance was negative in almost all the counties, by 1997 it was positive in 28 counties. The highest percentages were posted by the counties of Arad, Timis and Constanta, with higher negative figures for Maramures and Bistrita. The highest percentage of communities within a county with positive migration rates were recorded by Arad, Brasov, Braila, Constanta, Ilfov and Timis (between 70% and 76%), whereas the counties with the highest proportion of communities whose population was shrinking were Arges, Bistrita-Nasaud, Caras-Severin, Maramures and Suceava (some two-thirds of all rural communities).
A second demographic factor with a major impact on the dynamics of the rural population and on the size of the rural communities is the population growth rate, the difference between the crude birth rate and crude death rate. The dynamics of the rural population over 1977-97 (see Table 1.9) was characterized by a steady drop of the birth rate and increase of the death rate in both rural and urban area. The lower birth rate in rural area brought with it a decline of the young population – under 15
6 years of age – to less than one-fifth of the total (19.6%) by 1997. The higher rural death rate was a result of both the aging population structure in rural area and its higher death rates in every age group. It should be said that while the birth rate in rural area was at all times higher than in urban area, so was the death rate (about 1.7 times higher).
Table 1.9. Natality, Mortality And Infant Mortality Rates In Rural Area 1990 1991 1992 1993 1994 1995 1996 1997 No. of live births – thousand 157.8 139.9 136.4 132.7 132.3 126.9 123.1 126.9 Crude birth rate per 1000 people 14.3 12.9 12.9 12.7 12.7 12.3 12.0 12.4 No. of deaths – thousand 147.8 150.3 157.1 155.8 157.3 159.5 169.7 163.3 Crude death rate per 1000 people 13.4 13.9 14.8 14.9 15.1 15.4 16.5 15.9 Population growth rate per 1000 people 0.9 -1.0 -1.9 -2.2 -2.4 -3.1 -4.5 -3.5 Infant mortality rate per 1000 live births 4,693 3,604 3,505 3,507 3,599 3,031 3,200 3,173 Infant mortality rate per 1000 live births 29.7 25.8 25.7 26.4 27.2 23.9 25.6 25.0
The crude death rate and the infant mortality rate are indirect measures of living standards. Over time they were persistently higher in rural area, signaling a lower standard of living. The rising trend of population death rates in rural area was basically linked to causes of death commonly associated with old age such as circulatory diseases and tumors, but also to the higher incidence of diseases that are associated with a lower standard of living such as respiratory diseases and diseases of the digestive tract. By contrast, infant mortality sent mixed signals, having improved slightly after 1990 when the ban on abortions was lifted. However, the infant death rate is 1.3 times as high in rural as in urban area (25 infant deaths per 1000 live births in rural areas, compared with 19 in urban areas). The major cause of infant deaths was pneumonia, associated with a low level of sanitary education of the mothers, low hygiene standards and poorer heath care services in rural area.
The rural population decline has not been general. An analysis of population dynamics by community size shows that three tendencies are emerging. First, the smaller communities lost population to larger communities (especially those of over 5,000 inhabitants). Second, the closer to a city, the bigger the population loss of a rural community. And third, the rural population drop was more significant in the northeastern and southern counties and in the region of Transylvania. In Moldova, the population rise offset rural outmigration to keep the size of the rural population fairly constant. In Transylvania and Banat, migration of the young population, especially to Central and Western countries, was also important, especially after 1989. This phenomenon had both positive effects (transfer of money and business ideas to their families), and negative effects as the persons who migrated were those with a higher education and a more developed business initiative.
We measured the Table 1.10. Population Dynamics By Community Size population dynamics Community size: Population rise/drop in communities over 1977 - 1997 of each community Total Under 30% -20%-30% -10%-20% -1%-10% 0-29% over 1977-1997 and Total 2686 437 700 762 496 291 Under 1000 52 43 3 2 2 2 grouped the 1000-1999 residents 385 152 150 59 17 7 communities by size 2000-4999 residents 1666 238 490 527 286 125 (1997) and population 5000-9999 residents 550 4 57 167 182 140 gains or losses. The Over 10000 residents 33 0 0 7 9 17 results are shown in Table 1.10. This
7 contingency table shows the linkage between rural population loss and the size of the community. Just like the link between community size and death or birth rates, the larger the community, the higher the birth rate and the lower the death rate.
The dynamics of the rural community population during the past twenty years is illustrated, at an even greater level of detail, on Map No.1: Commune Population Dynamics , 1997 vs. 1977, Annex 1. The population drop was significant in the counties of Calarasi, Giurgiu, Teleorman, the southern part of Arges, Dolj, Mehedinti, and in Transylvania – Brasov, Sibiu, Alba, Cluj, Salaj, Satu Mare, Hunedoara, Timis. Table 1.11. Rural Population Structure By Age Group And Gender 1977 1997 Another factor that Age group (% of total): Total Male Female Total Male Female contributed to the loss of Under 15 years 27.1 28.2 26.0 19.6 20.2 19.0 population in the rural Between 15 and 59 years 56.2 56.6 55.8 56.4 58.7 54.2 area was the aging of its Over 60 years 16.7 15.2 18.2 24.0 21.1 26.8 population (see Table Dependency ratio 781 769 793 772 702 846 1.11). In the short span of twenty years, the population over 60 has increased from 17 to 24 percent of the total, while the young segment (under 15 years of age) has contracted from 27 to less than 20 percent. In absolute figures, the elderly outnumber the young, causing what population researchers refer to as an imbalanced rural population structure by age. Its causes are lower birth rates and selective outmigration with young people accounting for a higher percentage of the migratory flows to urban area. This trend brings a change of the dependency ratio emphasizing the growing needs for social protection of the elderly in rural area.
Population aging is widespread in both rural and urban areas; however, its scope is wider in rural areas. Three fifths of Romania’s elderly live in the rural areas. As expected, a higher percentage of the rural elderly are women, group with a higher live expectancy than men.
As concerns the distribution of the aging population across the territory, a linkage emerges between the percentage of aging population and community size. This can be seen in Table 1.12. Every rural community where the elderly account for over 40% of their residents is small, with a population below 5000.
Table 1.12. Proportion Of Elderly (Over 60 Years) In Rural Population As Of Jul-1st, 1997 Percentage of population aged 60 years and over Under 15 15-19 25-29 30-39 Over 40 Total 2686 135 440 786 652 549 124 Under 1000 residents 52 2 2 4 9 18 17 1000-1999 385 10 24 62 97 141 51 2000-4999 1666 62 225 504 460 359 56 5000-999 550 51 172 211 85 31 - Over 10000 residents 33 10 17 5 1 - -
Behind population aging are demographic and socio-economic causes, foremost of which are: female fertility, death rates (higher in rural area), a negative migration balance (mostly young or adult people migrated to urban area), average life expectancy (shorter in rural area). The evolution of these demographic indicators in recent years differed across the country, leading to disparities in population
8 aging by county. The share of the elderly is above the average in 17 counties: in two of them (Teleorman and Dolj) it is over 30%. Counties with a comparatively young rural population (under 20% of the total) are Constanta, Satu Mare, Maramures, Brasov, Sibiu. The population aging phenomenon is illustrated on Map No.2 – The elderly (60+) as a percentage of total, by commune, in 1997 in Annex 1.
One particular phenomenon that caused the loss of rural population the outmigration of the young and educated people. As already mentioned, the education levels are lower in rural areas (see Table 1.2). The population with no formal schooling or only primary schooling accounts for 27.6% in rural area, which contrasts with just 2.6% in urban area. On the other hand, higher school graduates are heavily concentrated in urban area, where over 90% of them reside. Lower schooling achievements mean a lower educational level of human resources in rural area.
We illustrated the regional patterns in educational achievements of the adults in the rural areas in Map No.3 People with primary education or less, in percentage of the population aged 12+ by commune, in 1992, presented in Annex 1. The data were taken from the 1992 population and housing census. The map classifies the rural communities in four groups, ranked by their percentage of less educated people. It shows a concentration of rural communities with lower education levels in two large geographic areas viz. Northeast, and the South. In Northeast, the counties with low educational achievements are Botosani, Iasi, Vaslui and Galati. In the South of the country, a significant proportion of the population with lower educational achievements lives in the counties of Teleorman, Giurgiu, Calarasi, Ialomita, Dolj and Mehedinti.
Before passing on to rural work force, let us sum up the major characteristics of rural population: it is shrinking, aging and less educated than urban population. All these are more frequent phenomena determinants in small rural communities far away from urban areas. By geographic region, the population drop is more significant in the south of the country and in western Transylvania, with aging affecting roughly the same parts of the country. The counties with lower education levels are concentrated in the Northeast and South border counties.
All these characteristics have direct rural development implications. We assume that the living standards are determined by:
Ø The quality of human resources ( approximated by education level);
Ø Opportunities for households to make good use of their factors of production ( approximated by location and distance to city);
Ø Other factors linked to rural community size.
By assumption, lower household living standards can be linked to lower educational achievements. Also, the living standard of a rural community can be linked to its size and proximity to large urban communities as essential prerequisites for households to make better use of their factors of production which vary inversely proportional with transport costs and proportional with the size of local and urban market. Finally, the geographic regions with shrinking population, lower birth rates, and rising death rates and outmigration may be in this position precisely because of falling living standards and our measure may be a proxy for the lower welfare areas.
9 1.1.2. Labor
Laboriv is one of the key resources for rural development. In any causal model, the standard of living will be influenced by the available human resources, by the size and quality of labor. To know what the current development level of rural area is like we must know the size and structure of its labor resources, the range of rural occupations, by activity and education level, and labor distribution by geographic region.
As shown in the preamble to this chapter, rural labor bears the mark of an almost mono occupational rural economy where the primary sector prevails. Farming is the main occupation in rural area. Labor is largely comprised of farmers – with self-employed and non-paid family worker status, and has an education level that is, on average, lower than in urban area. In the prevailing socio-economic conditions, all these are factors associated with lower living standards.
Table 1.13. Activity Rate And Unemployment Rate By Area Of Residence Activity Rate Labor Force Participation Unemployment Rate Total Rural Urban Total Rural Urban Total Rural Urban 1995 66.0 73.0 60.0 60.7 69.6 53.1 8.0 4.7 11.4 1996 64.8 69.9 60.5 60.4 66.9 55.0 6.7 4.3 9.2 1997 64.8 71.5 59.3 60.9 68.9 54.3 6.0 3.6 8.4
The activity rate is higher and unemployment lower in rural area (Table 1.13). The activity rate in rural area in the three years surveyed was by some 10 percentage points higher than in urban area. Differences were even bigger with the labor force participation rate (14.6 per cent age points in 1997). The rural unemployment rate – as defined by ILO – trailed the urban rate by some 5 percentage points during the same period of time.
By age groups, the working rate is far Figure 1.1. Activity Rate Of Adult Population, By higher in rural area for the young (15-24 Age Group and Area of Residence in 1997 years) and the elderly (over 50). This is something worth stressing. As can be 87 90 84 86 87 seen in Figure 1.1, the proportion of 80 76 72 70 young people in work is 61.9% in rural 62 60 59 area vs. 37.4% in urban area. As 52 50 concerns the elderly, only 5.5% of the % 41 40 37 people of retirement age (65 years and 30 over) in urban area are working, unlike 20 in rural area where the working people 10 6 0 in this group account for a high 52%. Total 15-24 25-34 35-49 50-64 65 years years years years years and over All other things being equal, a high Rural Urban working rate and lower unemployment rate would favor a higher living
10 standard in rural area. However, the positive effect of these factors on rural household welfare cannot offset other occupational factors that have an adverse effect on living standards such as the large share of the population working in agriculture, seasonality and high underemployment. In the authors’ opinion, the high rate of working population in rural area is a result of low farming technology, which makes rural people engage in low-productivity low income-generating activities. A large part of the rural population has no choice but to engage in low-productivity subsistence activities.
In rural area agriculture is the major occupation. About 70% of all employed people were employed in farming throughout the survey period (1995-98). This is not a bad thing in itself. However, it is the subsistence agriculture of household farms that makes farming an occupation predictably associated with lower living standards. The causes behind the low agricultural yields are beyond the scope of this section, but we could remind here the restrictive land market policies (farmland could not be sold or bought until 1998) and excessive farm input and output market controls which prevented competition and the emergence of a middle class of household farmers. Furthermore, low yields are associated with the new class of household farmers reinstated to land ownership in 1991, which have engaged in subsistence agriculture. This is so because the new class of landowners lacks the skills and knowledge required by modern farming. There is tremendous need for a new agricultural knowledge and extension system, but its development is still at an early stage.
Table 1.14. Structure Of Active Population, By Sector And Area Of Residence 1995 1996 1997 Rural Urban Rural Urban Rural Urban Active population in: Thousand 5,900 5,252 5,546 5,390 5,673 5,377 agriculture % 69.8 7.2 68.4 6.7 69.8 6.5 industry % 16.7 47.1 17.0 46.4 16.1 45.7 services % 13.5 45.8 14.6 46.9 14.1 47.8
The structure of the working population in Table 1.14 reflects the average for each area of residence. In Romania’s rural areas, there are wide disparities in the occupational structure from commune to commune. There are communities where industry and the tertiary sector lack altogether, and agriculture accounts 100 percent of employment. Such communities are expected to add lower value per labor unit, an assumption that would be tested in Chapter Three. The primary sector prevails in the East and South-West (Table 1.15) In these regions, more than three quarters of the rural population is working in agriculture, with Table 1.15. Structure Of the Active Rural Population By Sector And industry and services Region In 1997 accounting for a small share As % of total employed only (10%-12%). Region Agriculture Industry and Services Construction Another Rural Development North-East 78.9 10.8 10.3 Report (Urban South-East 74.6 12.8 12.6 Proiect/PHARE, 1998) South 64.4 21.2 14.4 presents two maps that use the South-West 75.2 12.6 12.2 existence or non-existence of West 67.9 14.0 18.1 North-West 68.2 16.9 14.9 industrial activities in rural Central 59.1 24.0 16.9 communities as a measure for Bucharest city, Ilfov 30.3 39.2 30.5 rural occupational diversity. The firstv is a map of industrial
11 activities in rural area, which shows that more complex rural industrial activities are concentrated in two counties, Suceava and Timis, and that some other industrial activities are located in a few communities in the counties of Satu-Mare, Bihor, Mures, Brasov, Covasna, Harghita, Gorj, Olt, Arges, Dâmbovita, Constanta. Most of the units operated in these communities are in the mining and food processing industries. The second map identifies the existence or non-existence of food processing in rural communities. This industry is strongly represented in Suceava, Vaslui, Constanta, Arges, Dolj, Timis and Olt counties.
The concentration of rural activities in the primary sector is associated to a different occupational structure compared to the urban areas. While over 90% of working people are salaried in urban areas, in rural areas two thirds are self-employed or non-paid family workers, most of them performing farm activities. The rural-urban occupational structure was stable in the past three years, as can be inferred from Table 1.16. Women account for 71.7% of non-paid family workers in rural area.
Table 1.16. Structure Of Working Population By Occupational Status And Area 1995 1996 1997 Rural Urban Rural Urban Rural Urban Working population, of which: Thousand 5900 5252 5546 5390 5673 5377 Salaried workers % 34.0 90.7 34.5 91.1 32.7 91.0 Self-employed % 38.2 4.6 34.5 4.6 34.7 4.7 Non-paid family workers % 25.9 1.9 29.8 1.9 31.5 1.9 Other (employers, coop) % 1.9 2.9 1.3 2.4 1.1 2.4 Working population of which: Underemployed % 5.6 1.2 6.0 1.8 4.4 1.0
The structure of the working population by occupation and geographic area indicates that in the mainly farming regions (North-East, South-East, South-West), the self-employed and non-paid family workers account for over 70% of the working population (Table 17). There is a direct correlation between the two determinants.
Table 1.17. Structure Of Employment By Occupational Status And Region In 1997 As % regional total Region Employees Employers Self- Non-paid Members of employed family labor associations Rural 32.7 0.4 34.7 31.5 0.7 of which by region: North-East 23.0 0.2 40.6 35.0 1.2 South-East 32.2 0.4 35.5 29.2 2.7 South 38.7 0.3 34.9 25.5 0.6 South-West 26.9 0.4 37.8 34.9 * West 39.4 0.7 23.4 36.4 0.1 North-West 32.9 0.4 32.9 33.8 * Central 44.2 0.3 28.0 27.2 0.3 Bucharest city, Ilfov 71.2 - 19.3 9.5 -
Rural employment is seasonal: it is lower in the first and last quarters of the year and highest in the second and third quarters. This pattern is largely due to the dominant farming activities – crop farming in particular – for which labor requirements peak at planting and harvest time. In urban area, the
12 seasonallity of economic activities is almost Figure 1.2. Activity Rate By Area of Residence absent. Rural seasonallity is illustrated in Figure 1.2. It may adversely affect the 65 expected household income, showing that 61 61 60 60 60 labor intensity is not the same all the year
57 round. 56 % 55 54 54 54 53 52 51 51 The lower education level of the rural 50 50 49 49 48 48 48 48 48 48 population signaled in the previous section can 47 45 be extended to the rural work force as well. Tr. I - Tr. II - Tr. III - Tr. IV - Tr. I - Tr. II - Tr. III - Tr. IV - Primary or lower secondary school leavers 96 96 96 96 97 97 97 97 account for 55.5% of the total working Total Rural Urban population in rural area, which contrasts with just 12.6% in urban area. By geographic area, the picture is brighter in West, North-West, Central and Ilfov where highly educated people employed in rural area makes up 2%-3% of all working population and vocational, high-school and post-high school leavers account for over 40% (Table 1.18). In the East, more than 60% of the working population have primary or Table 1.18. Rural Working Population, By Education Level And Region lower secondary education. The In 1997 education level is higher for Education Level As % of total rural and region’s total rural employees and for the University Vocational & Lower Primary (and male population than for both post-high secondary no schooling) school. the rural average and other North-East 0.8 35.8 30.5 32.9 categories. South-East 0.8 38.9 30.4 29.9 South 1.4 43.3 27.0 28.3 The distribution of the working South-West 1.9 37.9 30.4 29.8 population by age group and West 2.8 40.0 36.0 21.2 region points to one major North-West 2.0 38.6 36.7 22.7 characteristic of the rural work Central 1.8 48.1 32.0 18.1 force: it is aged. The proportion Bucharest & Ilfov 2.8 54.7 26.4 16.0 of the elderly in the working Table 1.19 Working Population By Age Group And Rural-Urban Area population in 1997 was 41.1% And Region In 1997 in rural area, three times as high Area/Region As % of total or regional total as that in urban area (13.7%). Region 15-24 year 25-34 year 35-49 year 50-64 year 65+ Labor aging differs across the Urban 10.7 28.5 47.1 12.6 1.1 country (see Table 1.19). The Rural 16.2 18.4 24.3 25.4 15.7 Southwest region seems to be in Of which by region: a more difficult position in this North-East 19.0 17.5 21.2 24.6 17.7 respect: the elderly (aged 50 South-East 17.8 17.5 24.9 25.0 14.8 years and over) who work in South 14.0 20.3 24.9 26.3 14.5 rural area make up some 48%, South-West 12.4 17.7 22.0 29.1 18.8 almost fourfold the young West 14.6 19.5 28.0 23.7 14.2 North-West 17.3 18.0 25.7 24.1 14.9 population in the 15-24 year Central 17.1 21.2 27.5 21.8 12.4 group. Also in the East, the Bucharest city, Ilfov 14.9 26.4 34.4 18.4 5.9 elderly account for a high 41% of the working population.
13 The employment pattern Table 1.20 Working Population By Type Of Employer And Area Of Residence by private/public sector 1996 1997 is different in the two Rural Urban Rural Urban areas. While in urban Working population (thousand persons) 5546 5390 5673 5377 area, the public sector of which, by type of employer: still contributes two Public 1474 3733 1307 3445 thirds of employment, in Private 3945 1385 4202 1537 rural area it only Other 127 272 164 395 accounts for one quarter or so. That was persistently so during 1996-97 (see Table 1.20). Most of the rural population recorded as employed in the private sector actually works in farm households. Accordingly, rural employment contributes 73.2% to total private-sector employment, with farming population accounting for 68.8%.
Another characteristics of the rural employment is the higher level of underemployment: its scope is greater in rural than in urban area and adversely affects the expected household welfare. Over 1995- 97, three to four times as many people were underemployed in rural than in urban area. In 1997, 82.4% of all underemployed lived in rural area. Underemployment was higher in the young (15-24 year) age bracket. Similar by its effect is part-time employment - people who have agreed to work a shorter week. Also this category is larger in rural area (87.5% of total) and comprises mostly women and non-paid family workers (they make up 92.3% of total part-timers).
Work Force Household Survey data would suggest that the typical rural household is the farmer household which consists of a self-employed and non-paid family worker – a woman, as a rule – with below average education level, affected by seasonality, employed part-time in farming, and with an above-the national average adult member age as a result of its large percentage of the elderly. This household type is more vulnerable.
How widespread is this farmer household in rural area across the country? Which are the areas where a large percentage of the population is employed in farming? Map No.4 The Share of Rural Population Employed in Agriculture in 1997 shows county variations. With the national average for rural area put at 70%, the counties where farming accounts for almost the only occupation are Vaslui (91.1%), Botosani (89.2%), Caras-Severin (85.9%), Teleorman (83.7%). Giurgiu (83.2%), Olt (83.9%). These counties are concentrated in the East and South of the country.
Before passing on to the next section, let us sum up the major characteristics of rural work force. Over 70% of the working population has farming as an occupation, works on the household farm, has an education level which is lower than in urban area and its members of non-working age – the elderly and the young - account for a larger proportion. By region, this is a more frequently recurrent pattern in the East and South of the country.
All these characteristics have direct implications for the rural development analysis and policy. We will assume that household living standards are determined by:
Ø the quality of human resources that make up the active population (using the education level of the working population as a proxy);
14 Ø marginal productivity in the sector or subsector providing employment to the active population; and
Ø the labor market experience of the working population, using their age as a proxy.
By this assumption, the less educated the working members of a household, the lower its living standard. Employment outside agriculture increases the chances for a household living standard to be higher than for farmer households. Finally, age may link up to a higher labor market experience, hence to better chances for a household to fare better.
15 1.2. Physical Resources of Rural Households
1.2.1. Land
The Land Law restored a good number of households to their farmland ownership rights. In 1997, more than half of the households (52.7 per cent) owned farmland.
The distribution of agricultural Table 1.21. Percentage Of Farmland Ownership, land ownership by area of By Household Type And Area In 1997 residence (Table 1.21) shows Households headed by: Total Rural Urban that 89.0 per cent of rural Employee 30.1 78.0 8.0 households are landowners Employer 24.2 65.3 10.7 which compares with 13.7 per Self-employed 35.0 76.8 11.8 cent urban households that own Farmer 92.1 94.1 80.7 such land. Predictably, most Unemployed 36.3 73.8 8.2 Pensioner* 65.4 94.0 21.0 farm households (92.1 per cent) Other 31.7 66.5 9.4 own farmland, but urban Total 52.7 89.0 13.7 households also account for a * Including agricultural pensioners fairly high percentage of agricultural landowners. Furthermore, pensioner households cut a large share (65.4 per cent overall and 94.0 per cent of those in rural area): this is so since, in addition to agricultural pensioners who fall in this category, it was the elderly who were the foremost winners of land restitution.
By area, rural households are clearly in the lead. Rural households that own farmland range from 65.3 per cent of employer households to 94.1 per cent of farmer households. In urban area, except for farmer households, the proportion of farmland owners varies from 8.0 per cent of employee households to 21.0 per cent of pensioner households.
In 1997, over 95 per cent of households who owned farmland worked it on their own or in farm associations (85 per cent of urban households) and only as little as 3 per cent (in both urban and rural area) leased it out. A distribution by land use shows that households own pre-eminently arable land (81.1 per cent), with pastures and hayfields making up 15.4 per cent of total, and vineyards and orchards 3.5 per cent.
Highly scattered land ownership has been a recurrent theme of this paper. Average landholdings per capita are illustrated on Map 5 in Annex 1. One may see that the averages are higher (about three hectares) in the West, North-West and Central part of the country, and lower (1.5 hectares or so) in the South and South-West, which makes quite a difference in poverty alleviation. As we are going to show in Chapter 2, poverty risk decrease monotonically with farmland ownership: the threshold beyond which the poverty risk drop significantly is two hectares per adult.
However, the earnings of farmland operators depend not only on the size of the landholding, but also on its quality. We sought to measure farmland quality variations by region with the help of the land area in arable equivalent-to-land area as such. The arable equivalent for vineyards, orchards, hayfields and pastures was built with the agricultural experts’ advice, the same that was used for the
16 implementation of the land lawvi . The ratio was used to draw Map 6 The arable area equivalent of one hundred hectares of farmland by commune in 1997, which is also presented in Annex 1. The map illustrates the somewhat higher productive potential of the agricultural land in the Baragan Plain and Romanian Plain where the size of arable land is sensibly larger than that of pastures or hayfields. 1.2.2. Livestock
Livestock are another key Tabelul 1.22. Average Number Of Livestock (By Category) For determinant of the rural 100 Households, By Region, In 1997 productive potential as a Region Horses Cattle Sheep & goats Pigs Poultry major source of farm North-East 24,7 65,6 219,9 98,2 1633,8 produce – foods and non- South-East 25,8 32,6 167,6 90,8 1727,9 foods – for on-farm use as South 24,8 49,6 111,9 122,4 1662,0 South-West 25,4 63,7 191,5 151,8 2310,8 well as for profit (Table West 19,5 61,4 265,2 220,2 1771,7 1.22). In 1997, on average North-West 24,7 96,2 275,1 170,6 1248,2 100 households – rural and Central 24,5 68,4 323,7 148,9 1271,1 urban – owned 13 horses, City of Bucharest, Ilfov 2,3 10,7 8,6 70,2 1071,8 47 heads of cattle, 154 sheeps and goats, 78 pigs and 965 tame birds. In rural area where animal farming is widespread, average per household ownership was put at 2 sheep and goats, 1.4 pigs and 17 poultry. Rural households also owned most of the horses and cattle (about one horse for every four households and two cattle for every three households).
Table 1.23. Variations From The All-Country Average Of The Animal Table 1.23 records the percentage Head Per Rural Household By Geographic Region In 1997 variations from the all-country - As Percentage- average livestock ownership per Horses Cattle Sheep & goats Pigs Poultry household by geographic region. North-East 3.3 7.9 8.4 -30 -1.9 A first noteworthy case is that of South-East 7.9 -46.4 -17.4 -35.3 3.8 Ilfov county where short South 3.8 -18.4 -44.8 -12.8 -0.2 distances to the capital city are South-West 6.3 4.8 -5.6 8.2 38.8 West -18.4 1 30.7 56.9 6.4 behind an average livestock head North-West 3.3 58.2 35.6 21.6 -25 per household well below the all- Central 2.5 12.5 59.5 6.1 -23.7 country average. Variations range City of Bucharest, Ilfov -90.4 -82.4 -95.8 -50 -35.6 from –35 per cent for poultry to – 95.8 per cent for sheep and goats. In the other regions, the picture is fairly balanced for horses, except in the West region, and for fowls – with the exception of Southwest where ownership is higher and of Central Romania where it is lower. As concerns cattle ownership, it is higher in the Northwest region, but lower in the Southeast and South regions. Sheep and goats prevail in the Central, Northwest and West regions, with the southern regions posting below-average figures; more pigs are raised in West and Northwest than in Southeast, South and Northeast.
However, livestock ownership of the average rural household is fairly low, equivalent to 2.2 large cattle units in both 1997 and 1998. Most rural households own livestock, in a quite diversified structure that signals the lack of specialization in animal breeding. Map 7 in Annex 1 shows Livestock distribution, in large cattle unit equivalent per capita. Livestock numbers are larger in West, North
17 West and Central part of the country as well as in Dobrogea (2.6 units on average) and lower in South and South East (1.8 units on average). This makes a great difference for rural poverty alleviation, as we are going to show in Chapter 2. 1.2.3. Productive Equipment
Ownership of farm equipment and Table 1.24. Farm Equipment Ownership By Area In 1997 machinery is key to higher Equipment in 100 households Total Rural Urban household productions. On this Tractor 1.3 2.1 0.4 item, household ownership is fairly Tractor trailer 0.6 0.9 0.2 modest, with low-technology Lorry 0.3 0.4 0.1 Mechanical mower 0.2 0.4 0.0 equipment (animal-driven ploughs, Plough 2.3 4.0 0.5 wagons and carts) outnumbering Tractor-driven plough 0.6 0.9 0.2 mechanical facilities (tractors and Cultivator 0.3 0.4 0.1 farm machinery). Employer Seeder 0.3 0.6 0.1 households are in a somewhat Cart, wagon 9.0 16.5 0.8 better position (3 tractors, 1 seeder, 1 tractor-trailer for every 100 households), even though landowners in this category account for a fairly low percentage. Just like with livestock, the distribution of farm equipment by area of residence clearly tips the balance to rural households which own most of the farm equipment (see Table 1.24).
The low farm technology is the explanation for a low productivity in rural area where the population is mainly involved in subsistence farming (and on-farm use of output) and only marginally concerned with commercial activities. Major reasons for this type of agriculture are: land scattering following the 1991 land restitution, the absence of a legal framework for a working land market, a shortage of capital and mistrust of the banking system even when concessional loans are provided.
1.3. Infrastructure in Rural Communities 1.3.1. Physical Infrastructure in Rural Areas
Physical infrastructure cannot be overlooked in rural development. A diagnosis of rural development includes the rural housing stock, communications network and public utilities. This will be presented herein where the stress falls on regional variations.
A. The Rural Housing Stock
By the end of 1997 there were in rural area close to 3.7 million dwellings, or 46.9 per cent of Romania’s total. The past six years have seen a rise in the numbers and quality of rural housing.
Over 1991-97 the number of dwellings, the number of rooms to live in and the living area increased more than in urban area and exceeded the all-country average. As a result, housing and housebuilding indices improved. Housing numbers in thousand inhabitants had risen to 362 in 1997, 4 per cent up on 1991 to better meet rural vs. urban demand. Also room numbers and living space per housing rose
18 during the interval. The Table 1.25. Rural Housing Dynamics * most relevant quality 1991 1994 1996 1997 indicator for housing – Dwellings – thou. 3583 3618 3655.6 3673.4 living space per capita – Rooms to live in – thou. 9170 9296 9422 9485 went up from 11.5 sqm Living space – thou.sqm 119853 121823 123880 124925 per capita in 1991 to 12.3 Dwellings in ‘000 inhabitants 344 353 360 362 Rooms to live in per housing unit 2.56 2.57 2.58 2.58 sqm per capita at end- Living area per dwelling – sqm 33.5 33.7 33.9 34.0 1997 (Table 1.25). Living area per capita –sqm 11.5 11.9 12.1 12.3 *At year’s end Housebuilding was the main factor that helped improve housing conditions in rural area. During 1992-97 105,302 new houses were built in rural area to account for 55.6 per cent of the country’s total and for 10.3 new dwellings in thousand inhabitants. The fact that rural housebuilding outnumbered urban housebuilding seems to suggest better rural resources to improve living and housing conditions. It should also be noted that new housing accounted for a higher percentage of the whole housing stock in larger communities with over five thousand residents than in the communities with a population of under two thousand.
It should be said that while the living space of a dwelling in rural and urban area is roughly the same, urban dwellings have more rooms on average, hence the better living conditions enjoyed by rural households.
What is indeed specific of rural housing are large variations in the size and physical characteristics of housing across the country (Table A 1.1 in Annex 2). First of all, in terms of dwellings in thousand inhabitants, 1997 marked an improvement in five counties Cluj, Hunedoara, Mehedinti, Salaj and Valcea where the figure ranges from 403 to 440 in thousand inhabitants. There are, however, nine counties with less than 340 dwellings in one thousand people in rural area: Bistrita-Nasaud, Constanta, Galati, Iasi, Ilfov, Maramures, Neamt, Satu Mare, Suceava - most of them lying in the North and East of the country. The lowest figures are in Bistrita-Nasaud (304 dwellings) and Constanta (306).
Since there are variations also in size in terms of average living space per dwelling – from 27.1 sqm per dwelling in Botosani county to 41.6 sqm per dwelling in Timis county and 41.4 sqm in Arad county, it can be concluded that the average living space per rural resident differs from county to county. This last indicator is also influenced by the size and density of the rural population of each county. Therefore, the living space per rural resident varies from 9.3 sqm in Iasi county to 15.6 sqm in Arad county and some 15 sqm in Salaj and Sibiu. Below the all-country average of 12.3 sqm per rural resident are 19 counties – most of which are in the North, Northeast and South regions.
As concerns the distribution of the living space per inhabitant by community it should be said that there are communities – about one-fifth of the total – where per capita living space is under 10 sqm. By contrast, the living space per capita exceeds 14 sqm in one sixth of all communities.
The structure of ownership type is another feature that differentiate rural from urban housing. Unlike in urban area, almost all housing in rural area is privately owned, with public housing accounting for an insignificant percentage. At the end of 1997, 97.4 per cent of rural housing was in private hands. Percentage variations by county range from 91.2 per cent in Brasov and 91.4 per cent in Timis to 99.0-99.7 per cent in Botosani, Dambovita, Giurgiu, Olt and Teleorman.
19 However, as concerns modern conveniences and amenities in the building (kitchen, bathroom, flush toilets) and access to such services as safe water, sewage, electricity and even central heating using pipelined gas) in rural area, the latest census data available – provided by the January 1992 housing and population census – make up a picture that is anything but satisfactory.
At the beginning of 1992, 81.8 per cent of all rural housing had a kitchen (a room which was only used to prepare food) but only a low 8 per cent had a bathroom (with or without a shower) and 5.8 per cent had flush toilets (and an appropriate sanitation system). As concerns the main utilities, 11.4 per cent had running water facilities (provided by a public system or by their own), 10 per cent were equipped with a sewage system, and only 4.4 per cent had central heating facilities or access to the natural gas distribution network. A high 93.6 per cent of rural housing had access to electricity.
In recent years, from 1992 to 1997, improvements were made to housing which increased access to running water, sanitation or natural gas distribution systems. However, the small scale of those improvements could hardly change the 1992 data, so that that rural housing has still a long way to go before it can provide conveniences comparable to those enjoyed in urban area. This is emphasized by the available data on physical infrastructure for public utilities in rural area.
B. Physical Infrastructure of Housing
In defining the rural living standard one cannot leave out access to public utilities in rural area: safe water supply, sanitation, natural gas supply for heating and cooking, electricity. In this respect, the gap between rural and urban area is still wide, in spite of the obvious concern to narrow it lately: the small investment effort as a result of low financial availabilities has achieved very little toward improving rural housing amenities . The physical infrastructure for public utilities in rural communities will be dealt with below.
The running water system. The rural running water supply network measured 13,550 km at end- 1997, or less than two-fifths (38.4 per cent) of the country’s total. The safe water supply system was extended by just 1366.2 km, or a little over one-tenth (11.2 per cent) in the last two years. As concerns safe water supply, the 181.8 million cu.m. distributed to rural users account for just nine per cent of the total amount supplied to the country’s population.
At the end of 1997, the public safe water supply to rural domestic users accounted for nearly four- fifths of the total amount of water used in rural area which in turn made up just 11.1 per cent of the country’s domestic use of safe water.
In late 1997 access to the public safe water supply system was restricted to just 1287 rural communities (out of 2686 across the country) and 2541 villages – or less than one-fifth (19.4 per cent) of all rural communities.
The rural safe water supplying facilities totaled 835.4 thousand cu.m. per day at the end of 1997 (barely 8.1 per cent of the country’s capacity). Furthermore, not every rural household in the communities with a public water supply system did have access to it.
As a matter of fact, the 1287 rural communities with a public water supply system (which, however, did not extend to every village thereof) accounted for less than one-fourth of the country’s rural population. These findings are upheld by the population and housing census conducted in early 1992
20 when as little as 11.7 per cent of rural households had access to safe water in the building (from a public or own system).
A distribution of rural communities by population size and mileage of safe water supply system in late 1997 is highly relevant. Table 1.26 shows that the larger the community (upwards of five thousand) the longer the supply network and conversely, smaller communities with up to two thousand inhabitants have lower access to water.
Table 1.26. Rural Communities By Population Size And Length Of Safe Water Supply Network In Late 1997 Size in terms TOTAL Length of water supply network, km of population No network Under 2-9.9 km 10-19.9 km 20-29.9 km 30+ km 2 km TOTAL 2686 1399 200 610 268 121 88 Up to 1000 residents 52 42 6 3 1 1 1000-1999 385 226 17 94 39 4 5 2000-4999 1666 909 123 374 153 67 40 5000-9999 350 216 49 129 72 45 39 Over 10000 33 6 5 10 3 5 4
As a matter of fact, less than one-fifth of the communities with a population of up to 1000 had a water supply system, which compared with three-fifths of the communities with a population from five to ten thousand, and more than four-fifths of the communities with over ten thousand residents.
Only in about one-sixth of all rural communities with a water supply system did its length exceed 20 km; in 15.5 per cent of these rural communities, the network was under 2 km long, supplying safe water to a few households only.
There are significant variations in the distribution of access to utility services by county (see Table A1.2 in Annex 2).
On the safe water supply network, it should be noted that it is longer in rural than in urban area in the counties of Salaj, Maramures, Buzau, Tulcea and Dambovita (ranging from 62 to 72 per cent of its length across the county).
Access to safe water supply systems by community varies widely: from 175 in Maramures county (or 83.3 per cent of all villages in this county) to 138 in Salaj (50.5 per cent of its villages) down to 75 communities in Dambovita (21.2 per cent of the total). Access to safe water is particularly low in Dolj county (just 3.5 per cent of the county’s water supply system which is confined to three rural communities) and in Bacau county (7.4 per cent of the all-county system’s length with only 20 villages, or 4 per cent of their numbers, having access to this service).
We may therefore conclude that the public safe water supply network, the size of the facilities and the amount of safe water supplied to domestic users are far from meeting the needs of either rural communities or rural households.
That being so, the rural population still lacks access to decent living conditions as it has to rely on unsafe water sources (wells, springs) with their effects on morbidity rates which are higher as a result of infectious and parasitic diseases.
21 Sewage system. Also on this item rural population is worse off than urban population. Sewers in rural area accounted for 864.1 km, or 5.6 per cent of the country’s total length. Only 358 villages or 2.8 per cent of all rural communities had access to sewage which averaged 2.4 km per village.
In terms of waste water treatment, in 1997 only as little as 1.1 per cent of the country’s total waste waters were treated in rural area, or 13,377 thousand cu.m., which comes down to about 102 cu.m. per day for each one of the 358 villages with access to sewage. Only 14 per cent of the villages with access to safe water had access to sanitation, too. A shortage of appropriate sewage systems is, among other things, an environmental liability.
The distribution of the rural sewage network by county varies significantly. In Ilfov county, 83.3 per cent of the sewage system is in rural area viz. in 21 villages (or one-fifth of all). Rural access to sanitation is low in the counties of Cluj, Mures and Prahova where it is restricted to 25-30 villages, or just 6-7 per cent of their total. Furthermore, in Harghita county no county has access to sewage, Alba and Ialomita counties have one village each with a sewage system, and Braila and Calarasi have two villages each where this service is available.
Natural Gas Supply System. In late 1997, 5591 km of Romania’s natural gas distribution system, or 31 per cent of the total, was in rural area, supplying gas to 697 villages that made up 320 communities; in 67 rural communities with a natural gas distribution network, natural gas was not supplied to domestic users.In terms of volume, only 1465 million cu.m., or10.3 per cent of all natural gas supplies, went to rural area, and as little as 7.9 per cent of the total was supplied to domestic users. While natural gas supplied to rural domestic users accounted for less than one-third of all natural gas supplies to rural communities, domestic users in 166 cities and towns across the country made up over two-fifths of the total (43.9 per cent). To sum up, only 1318.5 thousand cubic meters of natural gas per day was supplied to rural communities, to average 1892 cu.m. of natural gas per day per community. The natural gas supply to rural area by county puts Mures on top: almost half of its villages (221 rural communities) have access to natural gas. The rural natural gas distribution network of Mures county accounts for nearly three- quarters (73.5 per cent) of its total. A distinct position is that of Bistrita-Nasaud, Sibiu and Cluj counties where much of their natural gas distribution network covers the rural area, and yet their villages with access to natural gas account for lower percentages: 41.6 per cent of all villages in Sibiu, 12.5 per cent of those in Bistrita-Nasaud and 10.2 per cent of those in Cluj county. The overall better access to natural gas of these rural communities is above all due to reservoirs and drilling areas located in these counties. In absolute terms, however, the percentage of rural households with access to natural gas for heating or cooking is not very high here, either.
In sharp contrast to the above, there are 11 counties - most of them in the South and East of the country - where pipelined natural gas for domestic use is still unavailable to rural communities. The counties are: Mehedinti, Teleorman, Tulcea, Vaslui and Vrancea.
Access to Electricity. Household access to electricity is a major living standard indicator. From this angle, the rural population is in a better position as all 2686 rural communities are connected to the national or local electricity system. According to the latest data, the overwhelming majority of rural households (98.5 per cent or so) have access to electricity.
22 A low percentage of rural households that still have no access to electricity are to be found in a small number of villages that have yet to be connected to the national grid or are located in villages the electricity network of which must be extended. However, rural household needs are not fully met as long as frequency and voltage fluctuate widely as a result of inappropriate equipment and distribution systems in rural area. It should also be stressed that while in 107 rural communities (4 per cent of the total) only 65-90 per cent of the households have access to electricity, in 1928 rural communities (or about 72 per cent of the total) virtually every household has access to electricity.
Transport and Communications Infrastructure. The rural area also has to cope with a shortage of public transport and communications infrastructure that would help the delivery of appropriate services.
As far as transport is concerned, we would first refer to public roads – of county and community significance – which measured 58,478 km and accounted for four-fifths of the country’s public roads in mid-1997.
Of that total, just under one half (47 per cent) were county roads and 53 per cent were community roads. A very low percentage – 7.7 per cent of county roads and 3.1 per cent of community roads – were rehabilitated with more than two-fifths being cobbled roads. We may add that nearly three-fifths of community roads were cobbled, but there was still a quarter of them that were unpaved.
Inasmuch as rural access to national roads is concerned, it should be said that only about half of all rural communities (1462) – and about three-fifths of the rural population - had direct access to the main road network. The density of county and community roads in the territory was also low: at end- 1997, there were 27.3 km of roads in 100 sq.km of rural area.
As concerns communications, post and telephone-telegraph offices in rural area fail to meet demand.
In 1997 there were 7214 rural post offices or 90 per cent of all Romanian Post offices. Adding to them were 9805 mailboxes whose number dropped by 6.8 per cent in 1997, a drop which caused a compression of postal services in rural area. Postal offices are particularly few in five counties in the South.
Telephone and telegraph services in rural area were provided by 2182 offices, or some four-fifths of all units delivering such services; their numbers also had dropped in the previous year by 50 units (2.2 per cent).
Telephone units in rural area at the end of 1997 totaled 530 thousand, to account for 13.4 per cent of the country’s total but for just 52 units in 1000 rural people (the total includes telephone units of public telephone offices and legal entities in addition to those of households). Only in 1160 rural communities (or 43 per cent of their total) are telephone services available to every constituent village; in one-fifth of the rural communities less than one half of their constituent villages have access to telephone services. As concerns the distribution of telephone units by county, Map 8 Telephone Service Subscribers in ‘000 Inhabitants in Annex 1 shows that in the North- West region and part of Central Transylvania there is better access to telephone services.
Finally, access to radio and TV programs is not provided to rural households on a large scale (even though almost all rural communities have access to electricity).
23 The rural rate of subscribers to radio programs (by households included) which does not reflect the true ownership of radio and TV sets is put at 15 million and accounts for just 36.7 per cent of all- country subscriptions or 148 radio service subscriptions in 1000 rural residents. The 1.23 million TV service subscriptions make up 30.5 per cent of the all-country total and just 121 TV subscriptions in one thousand rural residents.
The lower access to radio and TV services of the rural population is an obstacle to its appropriate information and education.
1.3.2. Human Capital Improvement Infrastructure
Education and health are key to rural livelihood. They are the starting and the end point of major economic, social and cultural problems in rural area. The mere fact that villagers depend far more on farmwork for their living than urban residents cannot fully explain the low education level and poor health of rural population. Other factors also have a role. Their identification is important for rural development and improvement of human resource policy-making.
What exactly is the shape of the education and health care infrastructure of poor rural communities? What is the social and population structure of poor or rich communities in terms of human infrastructurevii? The answers to these questions may give us better picture of the potential human capital that makes the difference between rural communities (Table A 1.3 in Annex 2).
On average, there are six classrooms in 1000 residents, but there are wide case-on-case variations which range from 0 to 42 in ‘000 people. School infrastructure developed especially in the communities where high birth rates persisted over longer periods of time.
Demographic pressure alone could not account for a larger number of classrooms. Village isolation and social composition were also important. A better school infrastructure is reported in remote villages. Proximity to cities appears to have eased the pressure on rural communities to develop a school infrastructure of their own. The lower cost of transfer to city and better quality of urban education made rural residents prefer commuting to city schools. On the other hand, the community’s own resources compel recognition – notwithstanding the egalitarian and etatist socialist ideology that was in place for over fifty years: the Table 1.27. School Infrastructure In Rural Area, By Historical regression analysis indicates that Regions, 1996 school infrastructure is better in the Historical Region Classrooms in 1000 Teachers in 1000 communities where a larger share of inhabitants inhabitants its population is employed in non- Moldova 5,7 10,7 Muntenia 4,6 9,0 agricultural sectors. The parents’ Oltenia 5,5 9,4 salaried, basically non-agricultural Dobrogea 5,2 10,5 occupations was a specific pressure Transilvania 6,6 11,4 factor for school infrastructure growth. Crisana- Maramures 6,1 11,4 Even if the relevant distance-to-city Banat 6,5 10,6 and population composition variables Bucuresti 3,6 9,0 are controlled, regional development Total 5,6 10,3 per se seems to be a significant determinant of school infrastructure
24 differentiation: the communities within counties with a higher development index also have a better school infrastructure. The combined effect of the above-mentioned factors is behind the better school infrastructure of comparatively isolated rural communities with a high demographic potential and mainly non-agricultural population located in developed counties. Even if all these factors are controlled, regional culture and the history of regional education systems still have a significant part to play. The school infrastructure of the rural communities in the intra-Carpathian historic regions is better than in the extra-Carpathian regions (Table 1.27). Among the latter group, Muntenia and Moldova stand out by their low classrooms in 1000 resident indices.
Furthermore, a division by relief features shows rural communities in the hilly and mountainous area to be better equipped with school infrastructure than those in the plains.
The development of school services associated to education infrastructure has followed broadly the same pattern: more enrollments per teaching staff are reported in the rural communities of poor counties in the extra-Carpathian region of Muntenia than in the comparatively more developed counties in the intra-Carpathian regions of Transylvania, Banat, Crisana-Maramures). Naturally, where the elderly make up a large share of the population, which implies a lower share of school-age population, there are fewer teaching staff. i Bulai Alfred, Poverty Diagnosis and Community Development Opportunities in Alba, Botosani, Calarasi, Salaj, Tulcea and Vaslui judets – Synthetic Report, The World Bank , paper for internal use, Sept. 1997 ii Inputs and development level are not in a two-way relationship. Theirs is a two-fold determination relationship. We assume that the development level does not impact on the level of inputs instantly, which allows us a cross-section analysis. iii We did not take cross-border migration into account as its flows are far smaller than domestic population movements. The balance of cross-border population movements is negative, with emigrants outnumbering immigrants. Of late, both the size and balance of migration in and out of the country has been decreasing. As a share of cross-border population movement, the share of rural population in both emigration and immigration is small, under one-fifth of the total. Its impact on rural population dynamics is marginal. iv The analysis herein uses the data supplied by the Household Labor Survey - AMIGO v Identifies the following classes of communities: with no industries, with one industry, and with several industries vi The following coefficients have been used to transform one hectare of farmland into one hectare of arable-equivalent land; 1 ha pastures = 0.7 ha arable; 1 ha vineyards = 2 ha arable; 1 ha orchards = 1.5 ha arable; 1 ha hayfields = 0.75 ha arable, according to the “equivalency criteria of farmlands into arable equivalent-to-land” published in Annex 2A of the Law 18/1991). vii The phrase “human infrastructure” means infrastructure or services that help improve productivity (health, education and nutrition (Emmanuel Jimenez, Human and Physical Infrastructure. Public Investment and Pricing Policies in Developing Countries. The World Bank. Policy Research Department. Poverty and Human Resources Division. April 1994. While the main function of physical infrastructure is to increase physical capital, human infrastructure serves the aim of increasing the aggregate human capital at community level.
25 2. A Profile of Rural Poverty
One possible goal of a rural development policy is to reduce the severity and the magnitude of poverty in rural areasi. The aim of this chapter is to draw a poverty profile that documents these patterns, and trace their variation over time. The chapter signals that rural poverty is an important issue and that poverty alleviation should be an integral part of all rural development activities in Romania. The rest of the chapter, organized in five sections, presents the characteristics of the rural poor, showing which are the largest groups of the poor and which are the ones with the highest poverty incidence. We start, in section one, by presenting the methodology used in estimating poverty. In the second section, we examine what socio-demographic characteristics of the rural households are associated with greater poverty. In the third section, we present empirical estimates on the contribution of resource ownership to poverty alleviation. We check the robustness of our poverty measure in section four, comparing it with other apparent measures of welfare based on wealth. Finally, in the fifth section, we investigate the regional dimension of rural poverty.
In Romania, both the Table 2.1. Poverty in Romania: Evolution, Magnitude and Severity incidence and the severity 1995 1996 1997 1998 of poverty increased over Headcount rate, extreme poverty (% of population) 8.0 5.1 9.5 11.7 time ii (see Table 2.1). Headcount, poverty (% of population) 25.3 19.9 30.8 33.8 Poverty was a marginal Consumption Gap (% below the poverty line) 25.4 22.7 25.7 27.01 phenomenon at the outset of Poverty Severity, FGT2 (%) 2.4 1.5 3.0 3.56 the transition period, but Gini Index among Poor 0.1 0.1 0.1 0.14 became eventually a major Source: “Poverty in Romania, 1995-98”, UNDP 1999 for 1995-97, and own problem. Romania computation for 1998 experimented with gradual reforms for almost a decade, a combination of stop-and-go policies. These proved to be very costly, so that by 1998, GPD was still at 76% of its pre-transition level, with further declines expected in 1999. Falling living standards, notably in the level of current consumption per capita mirrored the decline in economic activity. Poverty was aggravated by the increase in inequality, due mainly to the new occupational risks – like unemployment, and the new opportunities – such free entrepreneurship. While in 1989 the number of poor in Romania was estimated to be under 900,000 persons, in 1998 over seven and a half million or 33.8% of total population were poor, of which more than 2.6 million were extremely poor.
Poverty in Romania is shallow, meaning that most of the poor are clustered not far below poverty line. This is illustrated by a low Gini index among the poor (0.1), and a relatively low consumption gap (the average consumption of the poor households is only 25% below the poverty line). Mainly because of this, poverty is highly elastic to GDP movements. A 3.9% GDP growth during 1996 took 1.2 million persons out of poverty, reducing the poverty headcount from 25.3% in 1995 to 19.9% in 1996. The 6.6% decline in GPD in 1997 pushed another 2.5 million persons into poverty, increasing the headcount to 30.8% in 1997. Hence, most of these are “transient poor”, people who have recently become poor due to declining macroeconomic performance. The resumption of growth will take them out of poverty. The severity of poverty also increased with GDP decline to 3% in 1997, or twice its 1996 level of 1.5%
26 In rural areas, the magnitude and Table 2.2 Poverty Headcount by Area of Residence the severity of the poverty is Survey Year: 1997 1998 greater than in urban areas (see Area of Residence: Urban Rural Urban Rural Table 2.2). Most of the poor live Poverty headcount, % 25.4 37.3 28.2 40.5 in rural area. Out of the 7 million Number of poor, '000s 3,124 3,822 3461 4149 poor in 1997, 3.8 million or 55% Share of total poor 45.0 55.0 45.5 54.5 of them, are living in rural area (accounting for 45% of the total Consumption Gap, % 24.2 27.0 24.46 29.13 population). The poverty Poverty severity, FGT2 (%) 2.2 3.9 2.54 4.78 headcount, measuring the Gini Index among Poor 0.1 0.1 0.129 0.133 Source: “Poverty in Romania, 1995-98”, UNDP 1999; own computation incidence of poverty, is 50% for 1998 higher in rural than in urban areas. The consumption gap, measuring the shortfall of the consumption of poor people below the poverty line, is significantly larger in rural area (27% in rural versus 24.2% in urban area), and the severity of rural poverty is twice as high. Poverty alleviation should be among the priorities of rural development programs. A large concentration of the poor in rural area has persisted throughout the transition, as evidenced by estimates in Table 2.2 (for 1997) or Table A2.1 in Annex 2 (for the 1995-98 period as a whole).
On the other hand, inequality, which may Table 2.3. Evolution of Inequality by Area of Residence take the poor deeper into poverty when (Gini) disparities grow wide, is not high in Year Total Urban Rural Romania (see Table 2.3). During the eight 1989 0.210 0.192 0.226 years of transition, it increased from 0.21 1995 0.308 0.295 0.317 in 1989 to 0.31 in 1995, decreased in 1996 0.298 0.290 0.301 1996 and 1997 to 0.28 and slightly 1997 0.284 0.275 0.292 increased in 1998. However, with a Gini 1998 0.301 0.289 0.312 coefficient of 0.28 in 1997 and 0.30 in Source: Computations based on LSMS 1995-98 and World 1998, Romania compares with the Bank (1997) Western European economies with a heavy welfare state, well known for their low level of inequality. Oddly, inequality decreased in 1997, the first year of a fast enterprise reform program, implemented in parallel with labor redeployment measures. This may be due to the hesitant course of enterprise reforms in 1997, and the possible over-supply of the social safety net package. By area of residence, the inequality was greater in rural areas throughout the transition period.
2.1. Poverty Measurement
In this study, we measured household welfare by the level of (current) consumption of the members of a householdiii. The consumption indicator includes all food, non-food and services consumed by the household members during a month, both purchased and produced on farm (own production). This indicator does not include imputed consumption of durables or owner-occupied houses, due to shallow rental markets in these assets. To compare the welfare of households of different sizes, consumption was divided by the number of “adult equivalents” existing in the household, using a scale derived by Romanian nutritionists. The resulting indicator, “consumption per adult equivalent”,
27 was used to rank the households according to their welfare.
All persons living in households with a level of consumption per adult equivalent below the poverty line were counted as poor. The poverty line used in this study is defined as 60% of the monthly average consumption per adult equivalent in 1995, equivalent to USD 40. For 1996 and 1997, the line was inflated on a monthly basis by the consumer price index, to account for seasonal variations in ROL purchasing power. To signal “extreme poverty”, we used a line defined as 40% of the average consumption per adult equivalent in 1995, equivalent to USD 27 per month. Those interested in the methodology are referred to Annex 3.
2.2. Rural Poverty and Demographic Characteristics of the Household
Poverty status is associated with the demographic characteristics of the household, notably with its size, the number of dependent children and the gender of the household head (see Table 2.4).
Throughout the world it Table 2.4. Rural Poverty by Household Characteristics has been observed that Year: 1995 1998 the poverty headcount Headcount % of Poor Headcount % of Poor increases with household size or No of children number of children. This no children 20.6 30.1 26,8 31,8 correlation holds also 1 child 34.1 22.3 45,1 24,2 for rural Romania, and 2 children 39.5 21.3 50,0 22,9 its capacity to 3 children 57.9 12.3 66,0 10,8 discriminate between 4 children and more 73.7 14.0 83,9 10,3 low-incidence and high- Household size incidence poverty 1 person 7.6 1.6 9,7 1,8 groups is the best. 2 persons 11.1 6.3 15,5 7,2 Families with no 3 persons 23.0 11.3 33,7 13,3 children face half the 4 persons 29.5 17.9 42,4 22,3 risk of poverty that 5 persons 41.8 19.6 54,0 20,7 families with one or two 6 persons and more 60.8 43.3 67,9 34,7 children do, and roughly Gender of the household head one third to one quarter male 33.5 87.4 41,5 85,3 of the risk of poverty female 28.3 12.6 35,7 14,7 faced by families with three or more children, Rural areas total 32.7 100.0 40.3 100.0 both in 1995 and 1998. Source: Own computation based on the LSMS 1995-98 In 1998, the magnitude of poverty is larger, but the relative incidence is similar to 1995. In 1998, the families with three or more children, accounting for 12% of total rural population, has the highest incidence of poverty: 66% of the families with three children and 84% of the families with four children or more are in poverty. Together, these groups account for 21.1% of the total rural poor and for 34% of the number of children living in rural area in that year.
28 The same is valid for family size. Households with five members face almost a 50% chance of being poor, and for those with six or more members the odds rise to two thirds. These two categories account for 55% of the rural poor in 1998. In contrast, single- or two-person families face a very low risk of poverty.
The gender of the household head is another characteristic of the rural family that significantly discriminates between groups of poor families. Our estimates show that female-headed households face lower risks of poverty as compared with their males counterpart, both in 1995 and 1998. While this may accurately measure the risk of consumption poverty that female-headed household are facing, there are other characteristics of this group that point to its vulnerability. Most of the female- headed households are elderly widows, who are facing not only the risk of inadequate consumption, but also health hazards. Their needs for care and old-age treatment is not accurately measured by our indicator. We consider the subgroup of elderly, single-female household, as a group at significant poverty risk. This group represents about 80% of the total female-headed households.
2.3. Rural Poverty and Household Resources
Poor rural households are poorly equipped with resources, meaning human capital and labor, as well as physical resources such as land, livestock and productive (agricultural) equipment. This result was empirically tested for the Table 2.5 Rural Poverty and Human Resources 1995 – 1998 period, and Survey Year: 1995 1998 found valid for each year. Headcount % of Poor Headcount % of Poor The level of human Total, Rural Areas 32.7 100.0 40.3 100.0 capital a household Education of the HH head possesses is difficult to no school or primary 37,7 40,9 41,8 34,2 measure accurately, but it gymnasium 34.5 35.2 40,5 28,4 is usually proxied by the vocational 33.9 16.0 43,7 22,1 level of education of the high school 21.0 6.7 38,3 13,6 adult members, by their post high-school 14.6 1.0 27,2 1,4 occupations (formal or university graduate 6.1 0.3 9,6 0,4 informal employment, Occupational Status of the HH Head formal or informal employee 30.8 31.7 39,2 26,9 entrepreneurship) and by employers 8.3 0.1 11,1 0,1 the experience they farmer 52.5 28.5 57,8 28,8 posses (measured by the unemployed 60.3 8.8 66,0 8,9 mean age of adult pensioner 21.3 26.1 27,4 28,1 members). Table 2.5 Average age of adults presents the variation in less than 30 years old 50.7 27.8 58,5 25,9 poverty headcount rate 30-40 years old 40.4 42.6 52,2 45,5 based of these 41-50 years old 31.2 19.4 39,2 18,7 characteristics in 1995 51-60 years old 19.1 5.9 24,3 5,7 and 1998. 61 years old and more 8.2 4.2 9,4 4,1 Source: Own computation based on LSMS 1995-98 Education is an asset, one of the main facets of the
29 rural households’ human capital. Its capacity to take people out of poverty is, for rural Romania, rather weak. Compared with the overall poverty headcount for the rural area, of 40.5% in 1998, only those households whose head has no education face significantly higher poverty risk (44%), and only those with post high-school or graduate studies face significantly lower risks (27.2% and 9.6% respectively). These extreme categories represent less than 10% of the rural population and less than 7% of the rural poor. The risk of poverty decreases monotonically with the level of education of the household head, but apart for the extreme categories mentioned above, it seems that the returns to education are rather low.
The rural labor market offers few opportunities to fight poverty. By occupation, only employer- and pensioner-headed households face lower-than-average poverty risks for rural area. Pensioner-headed households are the category with the next lowest incidence of poverty, but the second largest in terms of population.
Contrasted to these categories, formal employment offers a thin shield against poverty (headcount rate for employees is 39.2%), while informal employment or entrepreneurship is closely associated with the presence of poverty. More than 57% of the farmer, sole entrepreneur or unemployed headed households are poor. The relative scope of poverty has remained largely unchanged over 1995-98 (see Table 2.5 and Table A 2.3 in Annex 2). It seems that those who work as sole entrepreneurs in rural area are cursed to be poor.
Most of these facts, which may seem strange to the uninformed observer, are explained by the recent policies of asset redistribution, notably the land reform implemented in 1991. Land reform restituted up to 10 has of land to the former owners and their heirs, who were collectivized during 1948 and 1962. By design, land reform created a class of elderly landowners. Most of the heirs inheriting land received a fraction of what their parents had. Consequently, the younger landowners had smaller landholdings. As the land market was de facto banned for almost seven years, in 1998 the situation was very similar to what it was in 1991. Moreover, the ban on land transactions blocked younger farmers’ access to land.
Land in rural Romania is one of Table 2.6 Rural Poverty and Land Ownership the major assets a household has, Type of household Poverty Headcount Distribution of Poor and land ownership reduces 1995 1998 1995 1998 significantly the risk of poverty With: (see Table 2.6). Landless no agricultural land 37.8 54.8 15.6 16.4 households, representing 16% of less than 0.5 has 41.2 50.6 29.5 33.2 the rural population, face a 55% 0.51 - 1.00 has 31.6 43.2 11.1 11.3 chance of being poor in 1998. In 1.01 - 2.00 has 33.4 38.6 22.8 20.1 contrast, those with 3 hectares or 2.01 - 3.00 has 28.9 30.7 12.6 10.8 more, representing 8% of the over 3 has 18.2 21.7 8.3 8.1 rural population, face only a 22% Source: Own computation based on LSMS 1995-98 poverty risk. Most of the rural households, however, have less than 3 hectares of land and - valid for all landed households – the land they own is fragmented into many plots, imposing severe transaction costs on its operation. While contributing monotonically to a reduction of the risk of poverty, land ownership is, for the majority of rural landowners who own very little land, not enough to pull them out of poverty.
30 Another asset that almost all rural households have is livestock. As with land, the size of the livestock herd is small. The average household owns the equivalent of 2.2 cattle (in large cattle units). In average, a rural household owns 0.5 cattle, 1.2 pigs, 14 tame birds and 2 sheep. Table 2.7 shows that livestock breeding is not a specialized business; most of the households are raising livestock for their own consumption, using traditional methods. Poor households own, among other things, less livestock, as illustrated in the Table 2.7. The only exceptions are horses; the higher incidence of horse ownership among poor households is an indication of the widespread use of horse-pulled agriculture among this disadvantaged group. Given the low level of livestock per household, livestock ownership is not sufficient to move the poor out of poverty.
Table 2.7. Rural Poverty by Ownership of Livestock and Agricultural Equipment 1997 No of HHs Having: % of HHs having: poor non poor poor non poor Livestock Horses 0,2 0,2 17,1 15,5 Cattle 0,4 0,6 28,4 34,6 Sheep and Goats 1,4 2,1 21,0 25,7 Swine 1,0 1,3 57,6 62,6 Poultry 10,9 15,7 73,3 83,3 Agricultural Equipment Tractor 1,1 2,4 Tractor trailer 0,3 1,1 Lorry / ARO off-roader 0,3 0,4 Utility van 0,0 0,2 Plough 2,9 4,2 Tractor-driven plough 0,5 1,0 Harvester and combine 0,1 0,1 Cart, wagon 16,0 16,5 Number of Households 100,00 100,00 Source: Own computation based on LSMS 1997
Productive equipment is, in contrast, extremely scarce among all rural households. Few poor rural households have such equipment. Strangely, our poverty measure does not distinguish between non- poor with and without agricultural equipment.
So far, differences in occupations and access to land seem to be the main predictors of rural poverty. The ownership of other physical resources, such as livestock and productive equipment, is more or less equal in the first case and extremely skewed in the second case, to be able to differentiate the poor from not poor. We will explore in the next subsections the occupational status – land ownership interactions to determine their role in poverty alleviation for the major occupational categories in rural area viz. pensioners, employees and farmers.
A first look at differences in occupational characteristics of poor versus non-poor may be done by comparing the occupational structure of the two groups. Poverty is associated with a high proportion
31 of children in the family (a larger dependency ratio), a larger proportion of inactive adults and fewer pensioners.
In addition, the occupational mix of the active adults in the household is a helpful in predictor of rural poverty. There are important differences in the occupational mix of adults in poor and non-poor families. First, activity rates are higher in rural poor households than in non-poor households. 70% of the adults in poor families are working or seeking work, compared to only 50% for non-poor households. Such a finding indicates that poor households have no choice but to work in order to survive; the lower their earnings, the higher their labor supply. In the economists’ language, the rural poor are on the downward sloping part of the labor supply curve. Second, the employment structure of the rural poor indicates the lack of formal employment opportunities. Poor households tend to have fewer employees, but more farmers or unemployed in their membership.
Both from an analytical and from a policy point of view, it is useful to distinguish between three large occupational groups dominant rural areas and who, from the evidence presented so far, face different risks of poverty, namely the pensioners, employees and farmers. The causes for these differences will be identified below.
2.3.1. Rural Pensioners
A large share of rural population is aged, or is living in a pensioner headed household. More than one third of rural inhabitants are beyond 50 years of age, with one in four persons being 60 years old or more. Pensioners, the recipients of social security or agricultural social pension benefits, represent 26% of the rural population. Almost 41% of the rural population live in pensioner-headed households.
While most of these people, because of their age, do not have access to sufficient labor to support themselves, the incidence of poverty is lower in this occupational group than in other: 27.4% in 1998 compared with 40.5% for the rural area. Two interrelated factors help explain the pensioners’ lower poverty risk. First, most of the rural elderly receive pension benefits from their previous employment, or for previous membership in producer cooperatives. In the case of the latter group, signaled as highly at risk in previous studies, it seems that the 1997 readjustment of the pension benefits was effective in pulling them out of poverty. Second, the rural elderly were the main winners of the 1991 process of land restitution. From 1997, ownership of land also gave them access to input vouchers, adding to the purchasing power of households given the fungibility of money. Combined, access to pension benefits and ownership of land proved to be a sufficient shield against poverty risk for most of the rural elderly.
Pensions alone, on the other hand, are not enough to keep pensioner away from poverty. In 1997, we extracted pensioner households from those without employers or employees. These pensioner-only households were divided into families with at least one recipient of security pension benefits and those that included recipients of agricultural pensions only. Table 2.8 presents the results. There are three important facts to be noted.
Ø First, the risk of poverty is similar for the two types of pensioner households, if we control the amount of land the household owns. This finding confirms indirectly that the readjustment of agricultural pensions in 1997 was effective. The pensioners’ poverty risk is significantly lower
32 than the rural average.
Ø Second, agricultural pensioners have, on average, more land that social security pensioners, they benefit from higher agricultural incomes and input vouchers, and hence their group poverty headcount is sensibly lower. This is counter-intuitive given the low value of agricultural pension benefits – one-fourth of social security pension benefits for full length of service. However, with more livestock and farm equipment ownership per household and better agricultural knowledge, agricultural pensioner households manage higher earnings from farming.
Ø Lastly, the risk of poverty is still high for landless households or those with less than one hectare of land, but almost vanishes for those households owning three hectares of land or more.
Table 2.8. Land ownership and poverty among pensioners, in 1997 landless < 0.5 has 0.51 - 1.00 1.01 - 2.00 2.01 - 3.00 over 3 ha Total has has ha Pensioners from state-owned 37.9 38.1 29.1 24.4 20.5 13.7 25.8 enterprises Agricultural pensioners 36.9 33.4 30.8 21.6 20.5 11.1 20.7 Total rural area 50.3 48.9 40.2 33.8 26.5 19.0 37.3 Source: Computations based on LSMS 1997
For the pensioner-headed households, the main rural development problem does not seem to be poverty, or lack of an adequate social safety net. These households clearly raise different policy problems and options than working-age households, where lack of remunerative employment opportunities rather than lack of labor / safety net needs is the main problem. As we will see in sub- section three, one important issue with the rural elderly is – assuming that they are labor constraint – their large share in agricultural land ownership (two-thirds of total). With a sticky land sales market and insecure land leasing arrangements, it is very difficult to match the surplus land they own and the surplus labor of the younger farmers.
2.3.2. Rural Employees
Another large occupational group in rural area includes employees, representing 16.6% of total rural population but 38.4% of the active population of working-age in 1997. For the non-pensioner households, access to formal employment seems to be one major determinant of rural welfare, with poor households having less employees than the non-poor ones. However, the poverty headcount for employee-headed household is still high (36% in 1997), and persons belonging to this type of households represent one of the largest group of poor. In this sub-section we are analyzing formal employment in rural areas, examining the correlation between the employment characteristics and poverty. As expected, among the employee-headed households, poverty is associated with higher dependency ratios and lower wage levels. To see what factors determine the level of rural wages, we estimated a crude human capital regression on the rural employees sample.
What are the main occupations and activities or rural employees? As illustrated in Table 2.9. by occupation most of the rural employees are blue collars, providing skilled services. As expected, poverty is associated with unskilled worker status.
33 By activity, most of the rural employees Table 2.9. Distribution of Formal Employment in Rural are working in industry (41.4%), Area by Occupation, Activity, Ownership and Poverty followed by the civil service (17.2% in Status, in 1997 health, education and local (%) administration), agriculture (14.8%) and Poor Non Total transportation (10.1%). Employment in Poor services is extremely low, because Occupation of the Employee services are underdeveloped and where Managers 0.3 0.9 0.7 they do exist, they tend to be provided White collar 4.8 13.9 11.3 by family-type, informal ventures. Blue, skilled collar 63.5 63.7 63.6 Employees in agriculture or industry Unskilled workers 31.3 21.6 24.3 face a higher risk of poverty and are Activity more likely to live in poor households. Agriculture 17.6 13.7 14.8 Industry 39.8 42.0 41.4 The ownership status of the employer and the length of the work contract are Construction 9.9 5.5 6.7 the factors that differentiate most the Trade 7.6 8.3 8.1 poor from non-poor (Table 2.10.). Transport 10.1 10.1 10.1 Employment in a state-owned enterprise Civil service 13.6 18.6 17.2 is associated less with poverty, as well Others 1.3 1.9 1.8 as having a regular, long-term Type of Employer, by Ownership employment contract. State 65.9 73.7 71.5 Private 26.0 19.6 21.4 Transition changed the ownership Other 8.0 6.7 7.1 composition of employment, with Type of Contract 22.7% of employees having jobs in Open-ended 90.2 94.1 93.0 private sector in 1997. The impact was Short-term 9.8 5.9 7.0 slightly smaller in rural areas, where Source: own computation based on the 1997 LSMS only 21.4% of the employees work in the private sector as compared to 71.5% in the public sector and 7.1% in cooperative/mixed sector. By activity, private ownership dominates only trade (66.2%), and is comparatively developed in construction (37.3%). State employment still provides most of formal employment in rural areas, especially Table 2.10. Occupation of Rural Employees by Ownership, in civil service, transport, in 1997 telecommunications or industry. In % by type of property: State Private Others Total Low wage level is the main determinant of poverty. In order to Agriculture 68.3 22.8 9.0 100.0 examine the impact of various Industry 71.9 18.8 9.3 100.0 characteristics of the employee on Constructions 54.5 37.3 8.3 100.0 the wage he/she receives, we ran a Trade, retail and wholesale 26.0 66.2 7.9 100.0 crude human capital regression of Transport and telecom 86.1 11.0 2.9 100.0 the net wage (in natural logarithm) Civil service 92.5 5.6 1.9 100.0 over the age of the employee (as a Total 71.5 21.4 7.1 100.0 proxy for experience) and a set of Source: Own computation based on 1997 LSMS dummy variables for the type of
34 contract, level of skills, ownership of the employer, gender of the employee, activity and education (see Table 2.11). The reference category is a female employee with short-term contract, skilled, working for a private employer, in an activity other than agriculture, industry or civil service, and having at best primary school education.
Table 2.11. Determinants of Rural Wages Coeff. S.E. t-Stats Prob. Mean Elasticity USD Wage Net Wage (in ln.) 12.37 41.4 Predictors: Age (in ln) 0.17 0.01 16.17 0.00 3.764 0.17 Type of contract (1=Reg, 0=ST) 0.15 0.01 11.69 0.00 0.937 0.17 6.8 Unskilled (1=Yes, 0=No) -0.27 0.01 -36.02 0.00 0.270 -0.24 -9.9 Ownership (1=state, 0=other) 0.09 0.01 13.85 0.00 0.730 0.09 3.9 Gender (1=male, 0=female) 0.13 0.01 23.13 0.00 0.676 0.14 5.9 Agriculture (1=Yes, 0=No) -0.16 0.01 -13.11 0.00 0.140 -0.14 -6.0 Industry (1=Yes, 0=No) 0.03 0.01 4.67 0.00 0.416 0.03 1.4 Civil Service (1=Yes, 0=No) -0.17 0.01 -20.28 0.00 0.182 -0.16 -6.5 Gymnasium (1=Yes, 0=No) 0.04 0.01 3.45 0.00 0.252 0.04 1.9 Vocational (1=Yes, 0=No) 0.15 0.01 11.73 0.00 0.326 0.16 6.5 High School (1=Yes, 0=No) 0.27 0.01 22.03 0.00 0.233 0.31 13.0 University (1=Yes, 0=No) 0.60 0.01 41.83 - 0.040 0.82 33.8 C 11.33 0.04 252.4 - R-squared 0.26 Adjusted R-squared 0.26 F-statistic 697.7 Prob(F-statistic) 0.00 Source: own computation based on the 1997 LSMS
The equation is highly significant (probability of F-statistic is 0.00), although it explains only 26% of the variability of the net wage. All the predictors are significant (for 99% confidence intervals). Elasticity is computed for the continuos variable - age, and percentage changes are estimated for the dummy variables. The percentage changes indicate by how much the wage changes when the dummy variable changes from zero to one.
To make help the interpretation of the estimated equation, the last column of the Table 2.11 computes the impact that changes in the predictor variables have on the USD net wage. We will provide an example of how the marginal effects of the first three dummy variables should be read. The wage of the employee in the above reference category of 41.4 USD per month will increase by 6.8 USD if he/she switches to a regular type contract, will lose 9.9 USD if he/she is unskilled, and will gain 3.9 USD if he/she moves to a state-owned employer.
What is clear from Table 2.11 is that, in contrast to the rural area as a whole, returns to education are high for employees. These returns increase exponentially with the level of education. After education, next in importance are skills, followed by the type of contract, the gender of the employee and ownership of the employer. As for the type of activity, there are rents to be earned by those working in industry, as well as in transport, constructions or trade (the reference category) over agriculture or civil
35 service.
All these findings are consistent with the human capital literature, and are equally valid for rural and urban areas. What is somehow specific, for Romania as well as for other transition economies, are the “rents” extracted by state sector employees. One explanation to this fact is that the state sector is not a profit maximizer, the State Ownership Fund is unable to implement the required corporate governance or ownership control, so the state-owned firms still pay their workers more than their marginal productivity. The losses registered in the sector are one illustration of this effect. This finding provides empirical justification for the enterprise reforms that started in 1997. To enhance the efficiency of the state sector and eliminate over-pay or hidden unemployment in state firms, there is only one solution: privatization should go ahead full speed.
It is likely that the enterprise reforms implemented in 1997-99 would hit state sector employment. Two things are expected to happen. First, as some of the inefficient state enterprises will be closed down, or privatized firms will choose a more efficient capital/labor mix that requires labor firing, unemployment will rise. Second, for the employees of the recently privatized firms, the wage rate will drop to the level of private sector rate, in line to their marginal productivity.
This points to the vulnerability of rural employment to the enterprise reform packages. As most case studies document, state-owned enterprises are usually firing the “rural” workers first. This will put greater pressure on the rural labor market, already extremely frail. More and more people of active age would be pushed into the informal sector. To this sector we will turn our attention now.
2.3.3. Farmers, Access to Land and the Role of Agriculture
Household agriculture is by far the most important economic sector in rural areas. 20% of rural population do farming for living, and about 16-18% of the population belong to farmer-headed households. Other landed households, such as the employee or pensioner-headed households are involved in small farming as a second occupationiv. For all of these groups, agriculture plays an
Table 2.12. Food Self-Consumption in Rural Romania, by Occupation and Poverty Status Share in total consumption Share in food consumption Poor Non poor Total Poor Non poor Total Employee 43.2 35.9 37.5 56.3 60.6 59.5 Employers n.s. 31.3 31.9 n.s. 55.2 55.8 Sole entrepreneurship 46.3 36.9 40.1 56.0 62.2 59.6 Farmer 60.8 50.3 53.9 73.2 79.3 76.8 Unemployed 53.2 42.9 47.3 64.9 69.9 67.4 Pensioner 49.7 45.8 46.2 62.7 71.0 69.8 Total 50.7 43.0 44.6 63.4 68.8 67.5 Source: Own computations based on the LSMS 1997 extremely important role being a source of food security, as witnessed by the large share of own consumption in total food consumption. As indicated in Table 2.12., the share of own consumption is, for all rural households, landed or landless, between 55% and 80% of all food consumption. Own
36 consumption plays an important Table 2.13 Extreme Poverty by Occupation of the Household role in total household Head consumption as well, Poverty line: contributing between 30% and 40% of average 60% of average 60% of total household consumption consumption consumption. expenditure expenditure Poor Poor In addition, farming provides a Employee 9.5 36.7 shield against extreme poverty Self -employed 29.5 55.9 for that part of the rural Farmer 23.6 55.9 population that has access to Unemployed 32.0 64.4 wage incomes or pensions, and Pensioner 6.2 24.3 alleviates poverty for other Total 12.6 37.3 categories, such as farmers, Source: Own computations based on 1997 LSMS landed self-employed or unemployed (see Table 2.13.). Less than 10% of the employee- or pensioner- headed rural household are in extreme poverty. In the case of farmer-, unemployed- or self-employed headed households, agricultural income brings the number of persons in extreme poverty to only half the “official” figure for the poor.
We already noted the high poverty rate among the households that live on farming. We will investigate in this sub-section the likely causes of poverty among this group. We rule out shortage of labor as its cause. For the farmer-headed households, with an average of more than two economically active members per household, shortage of labor is not the main problem. More likely, poverty among farmers is due to lack of alternative employment opportunities. Lack of remunerative employment opportunities stands outs as the core reason for low income / low consumption, with considerably higher risk of poverty for all households without access to wage income. In the absence of such opportunities, working-age adults are pushed to farming, as a last resort occupation. By doing this, the land-to-labor ratio in farmer-headed households deteriorates, as does marginal productivity of farm labor. We will provide empirical arguments therefore.
Despite its important role in alleviating rural poverty, agriculture is not a source of prosperity, nor does it serve as an engine of economic growth. Private, small household agriculture has to cope with four major problems: a) excessive land fragmentation; b) a mismatch between access to land and access to labor; c) shortage of agricultural machinery and equipment; and d) poorly functioning input and output markets.
Excessive land ownership fragmentation. The 1991 land reform brought an unprecedented level of fragmentation of land ownership in Romania. After the land reforms in 1948, 36% of the agricultural holdings were less than one has in size, 27% were between one and two hectares and 37% over two hectaresv. Between 1948 and 1962, most of these holdings were forcefully consolidated into cooperatives. In 1989 there were in Romania 6,000 producer cooperatives occupying 8 million hectares of agricultural land. After the change of power in December 1989, most of the former owners took their land back from the producer cooperatives, in a “spontaneous” privatization. De facto, producer cooperatives were dismantled in 1990/91. In 1991, the legislative had no option but to issue a land law that sanctioned the earlier spontaneous repossession of the previous landholdings. It was then that producer cooperatives were dissolved “de jure”. Concerned with the re-creation of “landed
37 estates” in Romania, the legislative placed a 10 hectares limit Table 2.14 Land fragmentation in on the amount of land to be restituted. Also, landless Romania: 1997and 1948 compared households that used to work for cooperatives, or other 1948 1998 landless rural inhabitants, were granted ownership rights to Under 1 hectare 36 45 up to 0.5 hectares. The process resulted into a much more 1-2 has 27 24 fragmented ownership structure in 1991 that in 1948. The Over 2 has 37 31 figures from the 1997 LSMS suggest that 45% of the Total 100 100 holdings are less than 1has, 24% 1-2 has, and 31% are larger than 2 has (see Table 2.14).
Apart from land, the production cooperatives had other assets such as buildings, equipment and livestock that were restituted, when possible, to their members. Among these, livestock was easily restituted. As an example, we mentioned that some two million head of cattle were transferred from cooperatives to peasant households in 1991/92. However, most of the stock was slaughtered in 1992: by the end of that year, the cattle herd in peasant households was almost back to its 1989 level. Equipment and buildings were, in most of the instances, auctioned. Some of the buildings were simply destroyed, and the construction materials distributed to the members. At the end of 1992, one could see only the empty barns or other deserted buildings of the cooperatives, ghosts of a bygone age.
Mismatch between access to land and access to labor. By design, the principle of restitution resulted in a severe mismatch between access to land and dependence on land for a living, and between access to land and access to labor to operate the land. Most of the land was restituted to an elderly class, lacking adequate labor resources. Young and middle-aged rural households who depend on land for their living and who will have to make up the core of the future farmer population, lost out in the distribution of land in the wake of de-collectivization.
As illustrated in Table 2.15, pensioner households – accounting for 41% of total rural population – own 65% of the private land and work 63% of it. On average, one adult – including the pensioners in this category – works almost one hectare of land. If we consider that the workforce of the elderly represent a fraction of that of an adult aged up to 60 years old (say, 50%), then the ratio changes to
Table 2.15 Land and Labor Resources in Rural Romania, in 1997 % of population % of land % of land Hectares/Adult Hectares/Adult owned worked Equivalent Pensioner households 41.2 65.3 62.9 0.93 1.63 Employee households 30.8 15.8 147 0.12 0.14 Farmer households 18.9 16.8 18.6 0.63 0.66 The rest of rural households 9.1 2.2 3.8 0.31 0.33 Source: Own computations based on 1997 LSMS 1.63 hectares per “adult farmer equivalent”. In contrast, one farmer has, on average, less than two thirds of a hectare to work.
Poverty is associated with less land, especially for the farmer headed households. Poor farming households have only one third of the land per adult that non-poor farmers have. The same is valid for other “high poverty risk groups”, such as self-employed- or unemployed-headed households, or for pensioners. In the case of employees, the amount of land owned does not differs significantly
38 between poor and not poor households.
Shortage of agricultural machinery and equipment. We already indicated that most rural households have little or no productive farm equipment. The same is true for each occupational group, including farmers. Hence, the overwhelming majority of rural households have no choice but to turn to animal- drawn equipment or hire mechanical services. The situation was partly due to the design of socialist agriculture where producer cooperatives were dependent on the mechanical services of a specialized state-owned provider, now Agromecs. The new class of farmers inherited that dependence on Agromecs, that were not privatized before 1997/98. In time, the tractor, harvester and other machinery fleet became obsolete, and little efforts were made by the management to replace it with new pieces of machinery. While the state-owned tractor fleet decreased year after year, the private fleet increased steadily to account for 50% of the total in 1998. However, the level of tractor to land ratio in 1998 was only 80% of its 1989 level. Recently, the state-owned mechanical service providers were privatized, and there is hope that ownership will boost the efficiency of asset use.
Scattered land ownership, subsistence farming – a vicious circle. It is a well known fact that the farm input and output marketing system is poorly developed in Romania. An array of factors are behind this. On the demand side, the new class of farmers –poorly equipped with physical agricultural resources including land, livestock and farm equipment to fight poverty, lacking modern agronomic knowledge and cash-constrained -- have chosen a low risk–low return production strategy. For a poor farmer, a promising but risky output is not as good as a mediocre, but safe one. Immediately after the land reform, there was a shift from mechanized to non-mechanized crops, and from commercial to traditionally non-commercial crops. The area previously under technical crops grown by cooperatives – requiring agronomic knowledge as well as cash investments – was redirected to traditional crops. Thus, maize, which is used as both food and feed, and can be harvested by hand, has become by far the most important crop in terms of area cultivated. Similarly, labor intensive but high yielding crops such as potatoes, increased their importance. Wheat cultivation declined. These crops, including maize, wheat or barley, are produced by traditional technologies, based on the use of indigenous seeds, manure instead of organic fertilizer and almost no pesticides. Lack of knowledge and cash constraints, as well as a very risk-averse attitude, are driving such a behavior.
The over fragmentation of the agricultural land made planting and harvesting more difficult, and increase post-harvest costs such as transportation or quality control. This increase in the transaction costs is reflected in the larger buying versus purchasing margins for agricultural commodities. These margins, as measured by survey data, are high on average and vary widely. The 1997 average margins were 30% for wheat, 30% for maize, and 50% for potatoes. Such high transaction costs, combined with the risk-averse attitude of farmers, led to production diversification and autarchy. As a confirmation of the low risk – low returns strategy poor farmers have chosen, subsistence farming goes hand in hand with lack of specialization. Most of the farmers have more than four types of field crops, a wide range of vegetables, fruits and grapes. Also, they breed a diversified livestock, with most of the farms having two-three types of tame birds and pigs, and many of them having cattle or horses, too.
Poorly functioning input and output markets. There are several causes for the underdevelopment of farm output markets. Some are structural, rooted in a large and autarchic agriculture sector where 60% of the farmland is privately owned. This is a sector where market relations are ignored, as are the gains that could be made from market price variations across the country and over time; as a result output
39 demand and supply are low, which raises transaction costs. Most rural communities lack local markets, shops where farm inputs can be bought or farm output collection points. The main reason for this is the very low local demand and supply. Adding to this are macroeconomic causes. The over evaluation of the local currency, high interest rates to protect against inflation and subsidies largely directed to state-owned units have eroded the rate of return of private businesses, in rural area as well. Finally, some other causes are sectoral. Pan-seasonal and pan-territorial fixed prices for meat, milk and wheat over 1993-96 took private competitors out of the basic commodity and storage markets. It was not until 1997 that those policies began to be dismantled.
Of all factor markets, the land market stands out by its importance. The ban on farmland sales repressed the market where second best solutions had to be used (instead of sales-purchases). Until 1994 informal family associations or farmland companies were the only land-pooling option for joint operation. That kind of association developed spontaneously in the wake of de-collectivization as a reaction to the mismatch between household access to land, labor and capital. Of the rural households covered by the survey, 25% had contributed land to formal associations and 26% of the land had been operated in that way. By region, farm associations both formal and informal , are mostly in the plains where field crops prevail.
Land lease, the other way by which farmland user rights were assigned, was made into law only in 1994: prior to that, provisions were cumbersome and hardly secure for the lessor. After the land reform was enacted, land lease remained a marginal arrangement. In contrast, some 2% of agricultural land has been operated under sharecropping schemes and informal agreements that are unprotected by law. The land market law was reviewed in 1998 to allow for farmland sales and leases. It seems that an emerging land market has been growing fast since the ban on land transactions was lifted.
Table 2.16 Cobb-Douglas Production Functions For Private Farmers in Cluj County, 1996 Wheat Maize Potatoes Coeff. t-stat. Coeff. t-stat. Coeff. S.E. Labor -0.032 0.017 0.016 0.010 0.037 0.028 Land 0.980 0.041 0.952 0.021 0.711 0.045 Cash Inputs 0.037 0.033 0.032 0.016 0.134 0.034 R2 0.706 0.819 0.559 Returns to Scale Constant Constant Decreasing Impact of: Land fragmentation ------+ Use of certified seeds + + ------Source: Own computations based on the Cost & Returns Project, NCS & INCE-IEA 1996 Note: Gray areas mean coefficients not significantly different from zero at 5% significance level.
During transition, agriculture and farming became an occupational buffer. It absorbed partly the rural unemployed or back migrants. It also absorbed those that did not otherwise find formal employment. As a consequence, farming absorbed too much labor, which led to severe under-employment. We tested empirically the hypothesis of gross underemployment in agriculture, using the 1996 Cost & Returns Survey implemented by the National Commission for Statistics and the Institute for Agricultural Economics in judet Cluj. If this assumption is borne out, then marginal labor productivity is not significantly different from zero. This means that if we take out some labor from agricultural
40 activities, the production level will not fall. The results of the estimates are presented in Table 2.16.
We ran a Cobb-Douglas production function for three main crops – maize, wheat and potatoes – that hold a large share of private farmer output on the amount of labor (measured in days worked by household members and hired labor), cash inputs and area of land used. The inputs include the cash value of seeds, fertilizers and pesticides used. The equations also include two dummy variables indicating the use (or not) of certified seeds and small-scale (half of hectare or less) farming .
The same equation was used to test for constant, decreasing or increasing returns to scale. For wheat and maize, we found constant returns – meaning that doubling the amounts of factors will result in twice as large a production. For potatoes cultivated by traditional technologies, the returns to scale seem to be decreasing. Land fragmentation has a negative and significant effect for maize crops, and positive in the case of potatoes. Finally, we investigated the use of certified seeds to see if it has a positive impact on production. The empirical tests show that for both maize and wheat, the use of certified seeds boosts production.
Many of the estimates in this chapter as well as in Chapter 3 refer to 1996-1997. Examples to that effect are the analysis of rural labor markets, productivity of private farming or the consumption expenditure pattern in the next chapter. Lack of more recent data prevented us from making estimates for 1998. However, 1997 marked significant changes in the rural area which affected it and farmer households.
In 1997 Government began to implement an agriculture reform package supported by a World Bank Agriculture Structural Adjustment Loan. A key element of the reform package was the creation of a level field for farmers across Romania. The actions which taken in the early half of the 1997 included the removal of subsidies in the form of “premia” , liberalisation of the prices of agricultural commodities such as meat, milk and wheat, lower import tariffs, simple procedures for certified seed imports, removal of export controls. Instead of premia subsidies which distorted output and prices, a farm input voucher scheme has been implemented since autumn 1997. The size of concessional credit was reduced and its disbursement mechanism was changed to stimulate repayment. A second pillar of reform was the privatization, restructuring or liquidation of state-owned animal or crop farms, and of the firms upstream (mechanical service providers, certified seed suppliers) and downstream (grain storage). Designed to be implemented until June 1998, the privatization component was lagging far behind by mid-1999.
Most of the agricultural reforms initiated in 1997 reflected a major concern for the macroeconomic imbalances induced by the state-owned agricultural sector. Much of the emphasis of the reform was placed on creating a level-playing field, or privatizing state-owned firms. Some of the measures being implemented directly benefited small farmers, notably a) the removal of the ban on land transactions, b) the redirectioning of subsidies to the private sector via input vouchers, and c) the changes in the MAF’s role toward developing the private sector. The liberalization of land transactions facilitated transferability and opened the door to market-led farm consolidation. The input voucher system had three main goals which it achieved. First, to boost the demand for cash inputs among almost autarchic farmers, invigorating weak rural input markets. Second, it had a poverty alleviation component instead of purely agricultural concerns. Third, it sought to redistribute budgetary resources from a few state- owned enterprises toward small farmers. Two surveys undertaken in 1998vi show that these goals were achieved. Last but not least, the role of MAF was changed from almost exclusive support and
41 control of state-owned agriculture into a development agency providing public goods for the whole sector: it proved to be an important but time-consuming task that is ongoing. The recent reform in agricultural research and extension that aim to reorient the sectors to small private farm operators is a major outcome of this change of attitude.
We may expect these reforms to improve rural living standards and re-orient rural households to the markets. Our opinion is that reform in this area is yet to show its effects. We have more than one reason for this. First, the 1998-99 period was a period of economic contraction which caused urban demand – for rural output included - to shrink. Second, agriculture – as a major income-generating sector in the rural area – was affected by depressed world market prices especially for wheat, maize (since 1997) and meat (since 1998) . Finally farmer households are slow to respond to outside stimuli.
The above arguments should urge further sustained action – or so the authors think. While many steps have been taken since 1997 to make the rural area more dynamic, they stopped short of breaking the vicious circle of subsistence farming. Fast and determined action is needed to take the rural area out of its current stagnation. The last chapter of the paper outlines a strategy for change from rural poverty to rural development.
2.4. Robustness of the “Consumption” Poverty Measure
Consumption is only one aspect of the welfare of the household. The use of a simple monetary indicator to measure welfare (and poverty) somehow restricts the analysis of this complex phenomenon, as it does not take all household resources into account. We tested the robustness of this welfare indicator, investigating its ability to discriminate among poor Table 2.17. Rural Poverty and Ownership of Durable Goods, and non-poor living conditions, or in 1997 to detect differences in the Type of Durable % of persons living in HH with: ownership of durables. This poor non poor total investigation revealed that Cooking Machine 49.1 69.9 62.2 disparities in rural living standards Refrigerator 40.2 63.7 54.9 between poor and non-poor are not Washing Machine 19.4 33.9 28.5 so large, except for consumption. Dust Cleaners 2.8 11.7 8.4 B/W TV set 57.9 56.0 56.7 On the ownership of rural Color TV set 17.9 32.8 27.2 households with durable goods, presented in Table 2.17, one would Car 6.8 18.0 13.8 note two things. First, that a smaller Motorcycle 0.6 1.0 0.9 proportion of poor households Bicycle 17.8 24.7 22.2 posses durables, compared with Kitchen Furniture 37.5 51.2 46.1 non-poor households. A large Living Room Furniture 20.3 34.4 29.1 differential exists on the incidence Bedroom Furniture 39.3 53.2 48.0 of households with cars, furniture Table Total 100.0 100.0 100.0 and basic kitchen appliances. Source: Own computation based on LSMS 1995-97 Second, that a large proportion of non-poor households also lacks durables that an urban inhabitant
42 considers as “basic household appliances”, such as cooking machines, washing machines, dust cleaners, or “ready-made” furniture.
The living condition of rural households are synthetically presented in Table A2.3 in Annex 2. On the positive side, one would note that that 96% of them owns the houses in which they live, and 98.3% of rural dwellings have access to electricity. On the negative side, we note the general low standard of household amenities. More than 90% of them do not have bathroom, indoor toilet, sewage system or hot water supply, and are using traditional heating systems like wood, coal or oil. More than 80% of the rural dwellings use water from sources outside the courtyard. 55% of the rural dwellings are built with non-resistant building materials, like wood or trellis. Given this low level of household amenities, the differences between poor and non-poor households are rather small. Compared with their expected share in rural population of 37.3%, the dwellings of the poor are built more often from non-resistant materials.
There is a small number or rural dwellings without access to electricity (1% of total), and these are, usually, occupied by poor families. Also, among the few households that do not own their houses, more poor than non-poor live there for free or rented from the state; however the non-poor are more likely to rent their homes from private lessors.
Using consumption to measure welfare is not easy to distinguish between rural poor and non-poor with regard to durable possession or housing conditions, however. This may suggest that dwellings amenities are not determined by household income. We investigated the “village pattern” or “neighborhood” effect (due to inheritance, eventually). According to this assumption, the determinant for local amenities is the average level of amenities in a community, not household consumption. This is an assumption that seems to be supported by the available information.
2.5. Regional Dimensions of Rural Poverty
Data analysis shows Table 2.21. Regional Patterns of Rural Poverty that there are Poverty Headcount Distribution of Poor significant regional Regions poor non poor poor non poor differences in rural North-East 43.6 56.4 24.6 18.9 poverty. According to South-East 41.9 58.1 13.9 11.5 the information South 33.4 66.6 17.9 21.2 presented in Table South-West 35.2 64.8 12.3 13.5 2.21, rural poverty is West 34.8 65.2 7.0 7.8 more widespread in North-West 33.0 67.0 11.8 14.3 the Eastern part of the Center 37.3 62.7 10.3 10.3 country, in the regions Bucharest 33.0 67.0 2.2 2.7 North-East and South- Source: Own computation based on LSMS 1995-97 East. In contrast, the incidence of poverty is lower in Bucharest and in the Western part of the country.
Are these regions homogenous in term of the poverty incidence? An answer to this question is
43 provided in Chapter Three where we examine the intra-regional versus inter-regional differences in poverty rate.
2.6. Summary and Conclusions
We have drawn herein a profile of poverty which shows that rural poverty is an outstanding socioeconomic issue and poverty alleviation should be part of any rural development activities in Romania.
The characteristics of poor households in rural Romania are the same as elsewhere in the world: a high dependency ratio and low human and physical resources. By the occupation status of the household head, it is the farmer, self-employed farmer and unemployed-headed households that stand the highest poverty risks. The occupational status of the adult members and land ownership are the characteristics which, in addition to population indicators, clearly distinguish between poor and non-poor households. By category, the incidence of poverty is the highest among farmers and among employees.
Land ownership is an essential ingredient of the poverty alleviation, hence rural development, process. Currently it has the merit of shielding most of the rural population against extreme poverty. However, its important role in poverty alleviation notwithstanding, land ownership is neither a source of prosperity nor an engine of growth. Private farming which involves 60% of agricultural land has to cope with four major problems: highly scattered landownership; a mismatch between access to farmland and access to labor; the scarcity of farm equipment and machinery; and faltering factor and output markets. Besides, agriculture still acts as an occupational buffer of transition and absorbs the labor made redundant by urban industries and other sectors, even though its marginal productivity is zero. This high underemployment is the root cause of the low agricultural income that can hardly ensure small farmers a decent living.
Pensioners are the only large rural population category for which poverty risks are low. About 40% of rural residents live in pensioner-headed households that account for some two-thirds of private land ownership. They are the winners of the 1991 land reform and of the enterprise reform package. As rural pensioners may add the agricultural income to their pension benefits, the odds for poverty are low for this population category. Also, they have leased part of their land to landholding companies. It seems that elderly farmers have access to more land than labor.
By contrast, younger farmer households have access to far less land per adult farmer. As a result, agricultural income is low and most farmers are poor. It is the poverty status that has driven them to diversified, subsistence farming. Should they contemplate leaving agriculture, they stand little chance to find remunerative employment in rural area. So, they are held hostage to this agricultural pattern. A vicious circle has developed in rural area where poverty breeds more poverty.
A development strategy is needed to break this circle. First of all, the farming process for the land owned by old owners by younger and more efficient farmers should be supported through various market mechanisms such as leasing, sharecropping or sale. Second, some of the population currently employed in agriculture should either switch to non-agricultural employment. This would improve the land-to-labor ratio, hence marginal productivity to allow farmers to earn a decent living. However, for
44 this scenario to come true is required that farmers whose marginal productivity is zero or near-zero seek another decent income-generating employment.
The above analysis helped us find an answer to another set of policy issues concerning the timing of rural development actions. Under the current circumstances, the vicious circle of rural poverty is likely to persist. Rural poor households will continue to stay away from the input, factor and output markets. As much a cause as an effect, transaction costs for this sizable segment of rural entrepreneurs that own 60% of the land will remain high. Market forces on which economic gains depend and specialization which is the recipe for higher returns are unlikely to take hold in rural area any time soon. Concerted action is required to break the deadlock which in turn calls for appropriate policies and regulatory action, and the capacity to enforce it coupled by adequate financial resources. In short, a rural development strategy is required.
I Economists still debate what development path societies should choose, dichotomizing among one that alleviates poverty but results in a slower growth rate, or one that achieve the fastest growth at the expense of increased inequality and poverty in the short run. It seems that the answer to this question is function of the magnitude and severity of poverty. If poverty is a widespread phenomenon, a policy mix that leaves poor behind may be not politically sustainable, and poverty alleviation policies should be incorporated in the overall mix of policies. Similarly, society values like social cohesion may ask for policies that reduce the severity of poverty. High incidence or severity of poverty calls for its inclusion in the development policy mix. One should start, in determining the importance of poverty concerns in the overall rural development policy mix, with a diagnosis of the magnitude and severity of poverty in rural area. ii The poverty measures used in the paper includes the head count index or the incidence of poverty which measures the proportion of poor individuals in a population group (say the proportion of the rural poor in the rural population). Poverty is measured by the poverty gap , poverty index (also known as P1 or Foster-Freer-Thorbecke povert measure) poverty severity index (also known as P2 or the Foster- Freer-Thorbecke severity measure) and Gini index for the poor. The poverty gap is the difference between the mean consumption of the poor and the poverty line as a proportion of the poverty line. Poverty is defined as shallow if the poverty gap is small, and deep if the average consumption of the poor is far below the poverty line. The poverty gap index is the product of the head count index and the poverty gap. However, the poverty gap index does not measure the severity of poverty as it gives equal weight to the consumption deficit of all poor. The poverty severity index does it by weighting the poverty gap of the poor by the poverty gap itself. The Gini index measures consumption inequality: it varies from one to indicate perfect inequality to zero which stands for perfect equality. For further details on these poverty measures, see “Poverty in Romania: 1995-1998”, UNDP, 1999. iii All the poverty measures presented in the study were estimated on the basis of the Living Standard Measurement Survey data, from 1995 to 1998. We examined if regional differences in the purchasing power exist, and we recalibrate the data to account for non-response. Tests showed that no significant differences exist in the purchasing power of the ROL either by regions, or by area of residence. Consequently, we used the same line across all Romania. The survey is known to have a higher non- response rate among urban inhabitants, and high-income groups. To correct these biases, all the poverty estimates were produced using expansion (weighting) factors that bring the distribution of some survey variables close to the distribution of the population. The calibration of the expansion factors used in the study minimized the differences in the joint distribution of individuals classified by four variables: area of residence, gender, age group and judets. iv Estimates of a Household Labor Resource Survey and a Rural Financial Market Survey confirm this pattern.
45 v A Golopentia and P Onica, “Recensamantul agricol din RPR, 25 ianuarie 1948, Rezultate provizorii”, in “Probleme economice” 1948/3. vi The two surveys are The Farm Input Voucher Use Survey taken by INCE-IEA in January 1998 and the World Bank’s Rural Financial Market Survey of May-June 1998. The former points to the high rate of use of input vouchers by their recipients. The latter shows a significant increase in the number of farmer households who buy inputs (with vouchers included) to 80% in 1997-98, from an estimated 40% in 1996.
46 3. Living Standards And Development Resources
The first chapter of this study emphasized the uneven geographical distribution of development resources in Romania: labor and human capital, land, livestock, agricultural equipment, human and physical infrastructure. The second chapter pointed out that rural poverty, measured as lack of an adequate level of current consumption, correlates with the socio-economic characteristics of the household, but also has a regional dimension. This chapter investigates more rigorously the facts revealed by the previous exploratory analysis, using regressions to detect partial correlation between the availability of resources and rural inhabitants’ consumption.
This chapter is divided into four sections. In the first section, we examine the correlates of household welfare, linking the level of consumption per adult equivalent to the availability of household and community resources. This approach is employed to examine the marginal impact of each resource on household welfare. In section two, we reduce the number of predictors used in the model to four key factors that are thought to affect the level of household welfare. In the third section we use a variant of the model developed in the first section to predict the development level of all communes in Romania. The initial model, based on household survey data, covers only 242 selected communes, out of 2685, those selected in the surveyed sample. In order to extend the analysis to other communes, we develop a method to predict the development level of other locations, using administrative data as proxies for the independent variables in the initial model, so that a poverty map for the whole country can be prepared. This enables us to detect regional patterns in the development level of rural communities. Section four concludes. 3.1. Factors Influencing Households Consumption
The basic model estimated in this section starts from the observation that household consumption – a widely used proxy for household welfare – is determined, first, by the level and quality of the resources a household owns, and second, by the returns that household may derive from these resources. To compare the consumption of households with different sizes and demographic structures, consumption was expressed in an “equivalent adult” measure. In addition, some control variables such family size and the gender of the household head were introduced as right-hand side variables.
The simplest form of model to be estimated is:
C = aHC +bPR + dR + e where the dependent variable C is household expenditure (per adult equivalent), HC and PR are vectors of household resources such as education and physical assets (measuring human and physical capital respectively), R is a vector of variables that influence the rate of return on resources HC and PR, and a, b and d are coefficients to be estimated. e is the disturbance term.
The dependent variable in the model, C, is measured in natural logarithms. Current consumption includes consumption of food, non-food and services. This variable was regressed on three types of variables (HC, PR and R) which are explained as follows:
1. Household human capital variables. The first block of variables measures the human capital available at the household level. Human capital is usually measured by the level of education, experience proxied by age, the sector of activity and occupation. For education, we used as
47 predictor the average years of education of adult members. Experience was proxied by the age of the household head. For occupation and sector of activity, we used the number of wage earners, farmers, pensioners, unemployed and employers in the household. One would expect higher consumption be associated with higher level of education and experience. As for the occupational status of the active member of the household, one would expect consumption to be positively correlated with earning capacity. The magnitude of occupational coefficients will be used to rank the impact of various occupations on the expected level of consumption per adult equivalent.
2. Household physical resources variables. The second block of variables measures the physical resources the household owns, and includes livestock, area of land owned by type of agricultural land (arable, vineyards, orchards, pasture or hay), and productive equipment. For livestock, the predictor used is the level of the stock measured in large cattle unitsi. To measure both the availability of agricultural land and its quality, we used three predictors: the area of arable land, the area with vineyards and orchards, and the area with pasture and hay the household owns. For productive equipment, we use a dummy variable indicating the presence of some agricultural equipment. Finally, to signal those households that have land in excess of their labor, we used a dummy variable indicating that some of the land owned by the household was leased (mainly to agricultural companies),. One would expect a positive correlation between the availability of land, livestock or equipment and consumption. The sign of the land leasing dummy would signal if the returns on the land rented out are greater (positive) or smaller (negative sign of coefficient) than for the land worked by the household. For the block of variables related to agricultural activities – farm labor, land, livestock and presence of agricultural equipment, the model includes interaction terms that would allow us to test if such “resource complementarities” exist, and result in a higher level of expected consumption per adult equivalent.
3. Transaction costs and market opportunities variables. The third block of variables includes the population of the commune, the population of the nearest city and the distance between the commune and that city, the infant mortality rate in the commune and the development level of the judets. The first two variables are used as proxies for the size of the local, respectively for the nearest urban market. The distance between commune and city is a proxy for the transactions costs households incur if they want to sell their factors or outputs in a larger market, or to buy their input from that market. One would expect the return on resources owned by the household and hence the consumption, to be greater the larger the commune or urban market to which they have access (positive signs of the coefficients). In contrast, returns and consumption would tend to be smaller, the greater the distance between the commune and the city (negative sign of the coefficient). Note that distance alone is a crude indicator; quality of roads would provide a more refined measure, but the data are not available. The infant mortality rate for 1994-1996 is used as a general prediction for the development level of the commune. An index of the development level of the judetii in which the commune is located was used to control for regional determinants of local development, which can not be measured but influence all the households in a particular region.
Other variables introduced in the model, contained in a residual block, are the size and demographic structure of the household, the latter being measured by the age and gender of the household head. Household size, expressed in equivalent adults, was introduced- to control the economies of scale associated with larger households. The demographic structure of the household controls for the fact that consumption varies with age and gender.
Information on household welfare and privately owned, household resources have been collected through the Integrated Household Survey (LSMS). Information on the publicly owned resources was taken from the territorial statistics. We combined the most recent survey and territorial statistics information available, the 1996 LSMS and the 1996 database of territorial statistics.
48 The estimated model is presented in the Table 3.1. The model, estimated through least squares and corrected for the heteroskedasticity of the error termiii, is highly significant (probability of F-
Table 3.1. Household Consumption And Availability Of Resources
Dependent Variable: Consumption per adult equivalent (ln.) Sample: 16,283, Included observations: 16,146 Coefficient Std. Error t-Statistic Prob. Mean Elasticity HUMAN CAPITAL Average year of schooling of adults 0.012 0.004 3.179 0.001 7.281 11.739 squared 0.001 0.000 5.328 0.000 63.277 Age of household head 0.007 0.002 4.298 0.000 57.195 0.399 squared 0.000 0.000 -4.721 0.000 3503.123 Employer (number) 0.451 0.071 6.355 0.000 0.004 0.002 Employee (number) 0.163 0.007 23.358 0.000 0.503 0.082 Farmer (number) -0.002 0.008 -0.295 0.768 0.654 Pensioner or unemployed (number) 0.089 0.007 13.525 0.000 0.908 0.081 PHYSICAL CAPITAL Agricultural equipment (1=yes. 0=no) 0.102 0.069 1.474 0.141 0.027 Livestock (Large Cattle Units. sq. root) 0.232 0.008 30.072 0.000 1.360 0.135 Arable land (ha) 0.029 0.004 7.443 0.000 1.376 0.040 Vineyard and Orchards (ha) 0.121 0.020 6.179 0.000 0.064 0.008 Pasture and Hay land (ha) 0.004 0.007 0.562 0.574 0.258 INTERACTION TERMS (AGRICULTURAL RESOURCES) Land and livestock 0.000 0.001 -0.655 0.513 5.848 Farmer and land 0.004 0.003 1.719 0.086 1.158 Land and Equipment -0.002 0.006 -0.241 0.810 0.094 Farmer and Livestock -0.001 0.002 -0.607 0.544 2.177 Farmers and Equipment 0.086 0.025 3.410 0.001 0.029 Livestock and Equipment -0.018 0.010 -1.782 0.075 0.137 Land Leased out (1=yes. 0=no) -0.021 0.004 -5.879 0.000 0.374 MARKET OPPORTUNITIES AND TRANSACTION COSTS Distance to nearest town (km. ln) -0.025 0.005 -4.871 0.000 3.194 -0.025 Population of nearest town (ln) 0.039 0.005 8.345 0.000 11.521 0.039 Population of Commune (ln) 0.074 0.007 9.917 0.000 8.356 0.074 Infant mortality in the commune (1994-96) -0.001 0.000 -4.257 0.000 24.800 -0.021 Judet development index 0.002 0.000 5.650 0.000 -0.702 -0.001 CONTROLS Gender of HH head (1=male. 0=fem) 0.075 0.010 7.724 0.000 0.764 0.078 HH size. in adult equivalent (ln) -0.660 0.010 -65.109 0.000 0.581 -0.660 CONSTANT 10.433 0.094 111.287 0.000 R2 0.434 Adjusted-R2 0.433 F-stat 457.765 Prob(F-stat) 0.000 Source: Government computations based on LSMS 1995 and administrative data 1996 statistic is lower than 1%) and has high explanatory power. The independent variables are able to capture 43% of the variation in the consumption per equivalent adult. Such goodness of fit is considered good for a cross section.
Most of the predictors are significant at 1% significance level, and carry the expected signs.
49 Consumption per equivalent adult increases when the following variables increase: adult education, the age of the household head, the number of employers, employees and pensioners in the household, the livestock, the arable land, the vineyard and orchard land, population of the commune, population of the nearest town, the development index of the judet and when the household is headed by a man. Consumption per equivalent adult decreases when the following variables increase: land leased out, the distance to nearest town, infant mortality rate and the household size. Two interaction terms, between agricultural labor and land, and between livestock and agricultural equipment are only significant at 10% level.
Few predictors were not significant, meaning that their marginal contribution to the variation of the consumption per adult equivalent may be zero. Among these are the number of farmers in the family, the size of pasture and hay land areas, the presence of mechanical equipment and three interaction terms: between livestock and land, between livestock and the number of farmers and between land and agricultural equipment.
The insignificant impact of the number of farmers on the expected consumption per adult equivalent, controlling for other factors, signals to the low marginal productivity of agricultural labor. There are two facts that support such an interpretation. First, microeconomic theory tells us that a low level of land and capital leads to a low marginal productivity of labor. In the case of Romania, as documented in Chapter two, the level of land and machinery endowment is extremely low.
Second, most pensioners and employees have farming as a secondary occupation. The presence of a “farmer” in the family (that is, an adult having farming as his or her primary occupation) may mean the absence of an alternative employment opportunity. Farming, as a full time occupation, has a positive, statistically significant contribution to the consumption of the household, only for those with agricultural equipment (confidence interval 99%) and land (confidence interval 90%). From the model presented in Table 3.1, one can easily detect the significant versus insignificant predictors, as well as the direction of their impact (positive or negative) on the level of household consumption. In reading the table, it is difficult to evaluate the magnitude of a given variable’s impact on consumption, or to compare coefficients between variables. This is because different variables are measured in different units. Economists overcome this difficulty by transforming such coefficients into elasticity coefficients, which measure the percentage change in the predicted variables (in this case, household consumption) due to one percent change in a given independent variable, with other variables held constant.
We present in the last column of Table 3.1 the magnitude of the elasticity coefficientsiv for the significant predictors. The right-hand side of the model used three qualitatively different types of predictors: those expressed in natural logarithm format, those expressed as polynomials (of various degrees), and dummy variables. For the first type, elasticity is invariant to changes in the size of the independent variable, while for the second type it varies with its size, and in the table is computed at the mean value. Finally, the “elasticity” estimates for dummy variables show the percentage change of the consumption per adult equivalent relative to its mean when the dummy variable changes from 0 to 1.
The first thing to note from Table 3.1 is the low level of the elasticity coefficients. As these coefficients are “marginal returns on the available resources”, we can say that these returns are, in rural areas, very low. Except for the average number of years of adults schooling, all elasticity are below one, meaning that a one percent change in the (mean) level of any predictor would change consumption by less than one percent. The variable with the biggest positive impact on
50 consumption is “average years of adult schooling”, which shows that a one percent change in schooling would increase consumption by 11.7% (this is the interpretation given to an elasticity of 11.7). The next most important variable is “endowment with livestock” (elasticity 0.135) followed by “presence of more employees” or “pensioners/unemployed” (0.08), “population of the commune” (0.07), “endowment with arable land” (0.039) and “population of nearest town” (0.038).
There are at least two possible causes for the low marginal returns on rural household resources. The first cause, already mentioned for the agricultural activities, is the extremely low level of asset endowments in rural area. For instance, the average education level of the adults in the rural area is only 7 grades, the average farm size is 1.4 hectares, and the average level of livestock is about 2.4 cattle (large cattle units equivalent).
The second cause is the mismatch that exists in the availability of the factors of production at household level. These mismatches were policy-induced and policy-maintained, and were binding in the period reflected in our model (1996). For instance, land transactions on (restituted) agricultural land were banned up to 1998, and credit policies blocked the access of rural household to reasonably prices credit. To increase the returns on rural assets, these constraints need to be removed.
Higher level of rural assets not only desirable or needed, but is also possible, given the low starting point and the inefficiencies that currently exist in the rural sector. As an illustration, if a household with an average livestock herd of 2.4 cattle may double it, to the equivalent of 4.8 cattle, this alone will result in a 4% increase in the average household consumption. Given the low endowment with resources, substantial increases in their level is possible, and will result in significant increases in welfare. Finally, given the functional form of the estimated equation, an increase in the level of the predictor (ownership of the resource) will result in an increase of the marginal returns.
The signs and magnitude of the elasticity of the occupational variables confirm the exploratory analysis outlined in chapter two. Reading first the magnitude of the coefficients, one would see that one additional employer in the family would have the biggest effect on household consumption (per adult equivalent), followed by the presence of an employee, a pensioner or an unemployed. The presence of a farmer-only in the family does not raise the level of consumption.
For the rural area as a whole, the magnitude of the elasticity coefficients – that are weighted with the share of the respective occupational groups in total rural population –, are more useful predictors of what can be done over the short to medium term. Given the thin strata of employers, one percentage change in their number would not change much the expected level of consumption in rural area. In contrast, one percentage change in the employee group would increase expected consumption per adult equivalent with 0.085 percent. Similar estimate holds for a one percentage change in the number of pensioners.
Among the occupational variables, there are clear welfare differences between those in formal employment or with pension claims, versus farmers and informal entrepreneurs. For the group of pensioners, the low poverty risk signaled in the previous chapter, or the higher level of consumption mentioned in Table 3.1 is the result of a well functioning social safety net. Controlling for land ownership and for lower dependency ratio, having a pension claim is still as important for household consumption as being in formal employment. Formal employment, as described is chapter two, is largely non agricultural. It combines both the effects of having a wage contract plus having a non-agricultural occupation (in many cases, in addition to being a “part time
51 farmer”). In contrast, the presence of working-age adults having as occupation only farming does not led to an increase in the level of expected consumption per adult equivalent. This points to the need of diversification in rural occupations.
The third block of variables, labeled market opportunities and transaction costs, signal the regional dimension of development, the positive or negative synergy that exists because of spatial agglomeration of resources. Among the variables that are used as proxy for the market opportunities the rural households have, the strongest effect has the population of the commune, followed by the population of the nearest town. One may say that community resources are also relevant for the level of household consumption. Consumption is higher in communities that are located not far away from cities and benefit from better infrastructure, whatever the household composition and resources. The county development or growth level is a significant consumption determinant, no matter what the household or community characteristics are. The more developed a county, the higher household consumption. This finding supports the idea that consumption has household, community and regional determinants. Accordingly, rural development policies should be targeted at each level.
The socio-demographic structure of the household has an important impact household consumption. First, the household size has a strong negative effect on the expected level of household consumption. We noted from the previous section the high correlation between dependency ratio and poverty. Our model confirms the strong impact of the demographic structure on household’s consumption. The elasticity coefficient of “household size in equivalent adult” points to a 0.66% decrease in consumption for any percentage increase of it. For an average rural household of 2.4 equivalent adults, every new born child determines an increase of 12.5% in the household size. The negative impact on consumption per equivalent adult is a decrease by 8.25%. The remedy to such a situation can be an increase in child allowances. The survey data (1996) suggest that the measures taken in Romania in March 1997, to increase the level of the child allowances progressively with the number of dependent children, is a step in the right direction.
Second, the data suggest that gender differences are significant. Controlling for other variables such as age, occupation and availability of physical resources, consumption tend to be lower in woman-headed households. This may contradict the findings in chapter two, which signal a lower poverty rate among women-headed households, contradiction that is only apparently. Lower poverty rate among women-headed households is associated with some adverse factors of poverty such as a lower dependency ratio and a larger share of old women whose food consumption needs are considered to be smaller than men ones, for the same age. The model estimated in Table 3.1 considers that the consumption per equivalent adult of woman-headed households will be 8% lower than of men-headed households, demographic structure, human capital and physical capital being equal.
Lastly, age tend to have a negative impact on the level of average consumption per capita, even after the adjustment into adult equivalent. Age is a variables that captures two effects, the experience that household head accumulated, and the capacity to fructify this experience. The empirical test suggest that the second effect is stronger than the first. 3.2. Key Predictors Of Household Consumption
The regression model gives a very detailed image of the factors that are correlated with household consumption. Built on 25 independent variables the model covers a large area of possibilities which result from the combination of these variables. In most instances, however, the list of
52 influential factors is not very diverse. It usually clusters into “types of resources”. An empirical way to detect such resource mix patterns is factor analysis v.
Table 3.2. Patterns of Resource Mix Variable High human Livestock Land size and Developed Communalities capital farming tenure communes Average year of school of the adults 0.83 0,70 Age of the household (HH) head -0.77 0.29 0,70 No of employees in the HH 0.77 0,63 No of farmers in the HH 0.74 -0.22 0,43 Livestock 0.72 0.44 0,72 Sex of the household head 0.46 0.47 0,72 (1=male;0=female) Land owned by the HH (ha's) -0.20 0.33 0.75 0,64 Leased out land 0.70 0,38 Judets development index 0.79 0,54 Population in nearest town 0.59 0,62 Infant mortality rate 94-96 0.31 -0.57 0,46 Variation explained % 21.3 15.4 12.1 10.6 Source: Own computation based on 1996 LSMS and Territorial Statistics Database
We grouped the predictors used in the household consumption model through factor analysis. The results are shown in Table 3.2. The large number of predictors (25) used in the household model is reduced to four factors, labeled as “high human capital”, “livestock farming”, “land size and tenure” and “developed communes”. The factors were ordered by their capacity to explain the variation of the predictor matrix. The same factor analysis was used to compute four factor scores, one for each factor described above. To test the robustness of our findings, we introduced each score as predictor in a new multiple regression model presented in Table 3.3 that proved to be highly significant (probability of F-statistic = 0.0). This regression allowed us to see the effect these factors have on household consumption. In all cases, an increase in the factor level leads to an increase in the level of household consumption.
The first factor, Table 3.3. Association Between Factors And Household Consumption labeled “high human Coefficient S.E. Standardized t-stat Sig. capital”, is the one Coefficient that explains most of Constant 334,298 2,044 164 0.000 High human capital 95,689 2,044 0.331 47 0.000 the variation of the Livestock farming 71,826 2,044 0.249 35 0.000 predictors (21.3%), Land size and tenure 33,298 2,044 0.115 16 0.000 and “aggregates” Developed communes 25,205 2,044 0.087 12 0.000 three of the Source: Own computation based on the LSMS 1996 and Territorial Statistics household model predictors. It should be read as follows. Households with higher education levels are also comparatively younger and tend to have wage earners among their members. In contrast, households with lower education level have older age-members, most likely pensioners, and few or no employees. The implications of this dichotomy are profound. A rural development policy that tries to ameliorate the level of education of rural inhabitants should be tailored to satisfy the requirement of the two groups. For instance, roughly 80% of the adults aged 40 or less have at least 10 grades. In contrast, 80% of those in the 51-60 years old category have less than 8 grades and 60% of those over 60 years old have less than 4 grades. Among other things, the education policy should include a component for adult education.
53 The second factor, “livestock farming” signals the association between, on the one hand, livestock activities, and on the other, male-headed households having farmers among their active members. Livestock farming is found more often in farms with relatively large number of working age adults, and is less likely to be found in families headed by females.
The third factor, labeled “land use and tenure” signals an association between land size and land tenure. The more land a family has, the more likely that part of the land will be leased out (usually by placing it into agricultural companies / associations). Also, more land a family has more often livestock breeding is present. Leasing out land is associated with old age.
Finally, factor four identifies the profile of communities located not far away from cities which are also characterized by low infant death rates and implicitly greater access to urban health care services and, very likely, educational services, too. These factors are positively associated with household consumption. Put another way, faced with the same endowment with resources and the same initial stimuli, localities nearer to towns that are faced with similar resource endowments and the same initial conditions, tend to obtain better returns per the same amount invested, and therefore higher consumption. 3.3. Predicting The Development Level Of Rural Communes
The model in section one explored the partial correlation that exists between availability of resources and the level of consumption, normalized into a per adult equivalent measure. Among the “predictors” with high impact on the consumption of rural inhabitants were education, availability of a source of cash income, either from wages or from pensions, and ownership of land and livestock. At community level, consumption was influenced by the market opportunities available to trade output or factors of production, proxied by the size of the commune and neighboring city, and the distance between the two. At the regional level, consumption was positively associated with by the general development level of the judets and by lower infant mortality rates.
The estimation of the model required availability of two types of information, gathered through surveys at household level and from administrative statistics at community level. Survey information is, by its very nature, incomplete. For instance, such information was available for 242 rural communes out of a total 2,688. Administrative statistics, on the other hand, were available for all rural communes and were quite rich. We used the administrative statistics data to build proxy variables for the household variables estimated in the model in section one. With these proxy variables, we built a “pseudo-model” that was able to predict quite accurately the level of the household welfare in the communes covered by the LSMS.
We used this “pseudo-model” to predict the level of welfare for all the communes of Romania. However, as mentioned in Chapter Two, the average consumption per adult equivalent at commune level is just one of the welfare measures, and is only able to partially capture other aspects of the well being such as dwelling comfort or ownership of durables. To capture better the level of commune welfare, we broaden the welfare indicator to include more facets of well being than simply private household consumption. An index of community povertyvi was constructed for each of 242 communes of LSMS by aggregating the infant mortality rate (1994-1996), the out- migration rate (1994-1996), the general fertility rate (census data, 1992) and poverty rate at commune level. Higher values of the index are associated with poorer communesvii.
This commune poverty index was regressed on the same set of predictors as in the household
54 model, using proxies for household level predictors, and variables specific to each commune. The proxy variables can be interpreted as the characteristics of the average family in a commune. The predicted level of welfare is an “unconditional” estimate, reflecting the socio-economic characteristics of the population of that commune.
For most of the core Table 3.4 Community Poverty and Community Resources predictors that are Regression Standard Standardised household specific, coefficients errors regression coefficients one can find good Constant 11.60 9.34 1.24 proxies in the administrative Proportion of farmer population in total working 1.96 0.38 0.37 population 1992 ( conversion via square root) statistics database. Proportion of population over 60, 1996 -0.53 0.09 -0.36 Instead of the Proportion of primary school leavers, 1992 0.21 0.09 0.20 average level of Proportion of secondary school leavers, 1992 -0.64 0.19 -0.24 education of the Proportion of arable land in total farmland -0.63 0.27 -0.13 (conversion via square root) adults, we computed County development index 1995 -0.15 0.07 -0.15 (from the 1992 (for details see Note 5) census data) the Average farmland area per household -0.44 1.00 -0.03 population (conversion to) proportion Telephone subscriptions per 1000 people,1996 -0.33 0.19 -0.09 graduating from (conversion via square root) primary and Population of nearest city with a population of over -0.56 0.71 -0.04 30,000 . (ln conversion) secondary education R2 0.48 levels. Occupation Source: Computation based on the 1996 LSMS and Territorial Statistics Database, was proxied by the NCS proportion of farming population in the total active population of a commune, while the proportion of pensioners were estimated by the percentage of people aged 60 or more. This proxy is also expected to partly capture the gender effect, since women have a longer life expectancy than men do and would be over represented in this age group. The average household endowment with physical resources was directly determined from administrative data. For example, the availability of land resources was determined by computing the average size of private farms (in hectares), using the proportion of arable land in total agricultural land as a control for land quality. The data allows computation of livestock (expressed in large cattle units) per capita. We did not include a proxy for the average agricultural equipment stock, because it was only weakly correlated with household level consumption. The rest of the predictors in the household model were “commune specific”, derived from administrative statistics database, and this data was available for all communes. We lumped together the two group of predictors – both household level and commune level - and estimated the model, with results presented in Table 3.4. The model, adjusted for heteroscedasticity of the error term, has high explanatory power. The explanatory variables account for 48% of total variation in the community poverty. Note: the dependent variable is described in the vi-th endnote. Also, the model has a high level of statistical significance. Among the predictors strongly associated with higher levels of commune poverty are: the proportion of farmers in total active population, and the proportion of primary school leavers. Among those associated with lower levels of poverty are: he proportion of aged population (over 60 years old):the percentage of secondary school graduates (99% confidence intervals); and the percentage of arable in agricultural land (99% confidence intervals).
55 The commune model duplicates, as expected, the findings of the household model from section one. Poorer communes are those with a higher proportion of the farming population in total employment and Map 3.1 Rural Romania By The Development Level Of Its Communes with a higher percentage of adults with no more than primary school. The higher the proportion of adults graduating secondary school, or the higher the endowment with physical resources per capita, the wealthier the commune is. Location variables influence the commune welfare in the expected direction: the closer one is from a town, or the simple location of a commune in a more developed judets decreases the likelihood that commune will be a poor. Rural poverty is the deepest farming communities with low school achievements and poor communications infrastructure located in hilly and mountainous areas and in counties scoring poorly on the development index (Table 3.4). Secondary education helps to significantly reduce rural poverty. Just as in the case of households, community poverty data show that villages with larger arable landholdings are better off.
We used the regression model outlined in Table 3.4 to determine the expected level of the poverty in all the communes of Romania, conditional on household and community resources.
One of the possible utilization of the model is for identifying the communes which are not so advanced in term of rural development. The components of the rural development programs having as purpose to fight against poverty should be focused for increasing the efficiency of investments in these communes. In Annex 4 we are presenting the list of the less developed communes, placed in the inferior quintile of the distribution of the community poverty indicator. Table A4.1 presents the dimensions of the under-development phenomenon at each judet level, expressed related to the under-developed commune weight in the total of the communes within the judet, respectively the weight of the population within these communes in the total population of the county. Table A4.2 lists these communes for each county.
Although the rural under-development is widespread, our analysis shows that it is more severe in some localities. We identified the rural areas in which the poverty incidence is the highest, the life standard is low, the living conditions are only basically and the public services are not satisfactory. These issues are suggesting that a policy of rural development is necessary and the focus should be on these areas.
As resources are limited, it is always preferable to be concentrated on the poorer rural regions, localities or individuals having the biggest need. In order to increase the efficiency of the public
56 expenditure for rural development one solution is to identify the targets for the investments. There are different ways to select the targets: by defining a set of eligibility criteria allowing to select the right beneficiaries – the perfect selection of the beneficiaries, or another approach is the territorial one.
Which one of the solutions is the best one? The classic argument is offered by the economic literature. Although the perfect selection of the beneficiaries is preferred is reducing the costs of resource “leaking” (as the cost of supplying benefits towards the non eligible beneficiaries), these solution impose the existence of a control mechanism for the separation of the target group (for example the poor people in the rural areas) from the rest of the population. Such a mechanism requires the existence of an administrative network and involves running costs. If these costs are small (due to the fact that the distinction between the poor people and the others is hard to be made), or if they are already covered (had been already included in order to reach another target, as it is the case of the income tax used in the developed countries, allowing its use for selecting the poor people with a very low additional cost), the best option is the perfect selection of the beneficiaries If not, the selection costs could be bigger than the advantage of excluding the “not wanted” beneficiaries
The opinion of the authors is that under the current situation in Romania the perfect selection of the beneficiaries is not possible. This is the reason why the selection of regional targets represents the economic alternative. The main argument against selecting perfect targets is the existence of an important informal economy for which the basic information are missing, making hard to establish who are the Map 3.2 Rural Romania by the Development Level of its Judets eligible beneficiaries. Considering hat poverty had been shown as being a clearly localized and stable in time phenomenon the geographic position could be used as an indicator for the target areas, with a reasonable percentage of resource “leaking” towards the “not wanted” beneficiaries. We suggest that the pilot rural development projects to start in these less developed areas.
Which are the communes with the highest rural poverty indicator within the country? This information, was subsequently mapped (see Map 3.1), allowing the identification of regional patterns, such as poor, non poor and relatively developed areas.
The less developed communes are the ones with the highest index, indicating the presence of welfare adverse factors. Poverty is most severe in communities located in the North-East of the country, but some large poverty pockets exist in the South as well.
57 We grouped the (under) development index at judet level in Map 3.2 “Rural Community Poverty Index”, by computing the aggregated index weighted with the population of each commune. The first things to note is that the judets in the center and western part of Romania are the most developed rural areas. The exception from this quite strong regional pattern is Constantza, probably because it is also a main tourist destination and includes the main sea harbor of Romania. At the other extreme, among the most underdeveloped judets, one would count Botosani, Vaslui, Vrancea and Teleorman. The second thing is the heterogeneity of rural development. The recent division of the country into “development regions” has not succeeded, according to the data presented in Map 3.2., in merging areas with similar levels of development into relatively homogeneous regions. In fact, these “development regions” include more and less developed areas together. An interesting hypothesis to test is whether interregional differences (differences between regions) are higher than intraregional differences (differences within regions), as suggested in Chapter Two. To test this hypothesis, we aggregated our development indexes at regional and sub- regional levelviii.
Such a hypothesis is refuted Table 3.5 Intra-Regional Variation of Rural Development by our data (see Table 3.5.). Development region Rural development Average subregions, community There are wider intra-regional constituent counties poverty differences in the SV NT BC 14.24 development level of rural NORTH-EAST BT VS IS 19.38 communities, than inter- GL CT BL 14.19 regional differences. The new SOUTH-EAST BZ VR TC 15.39 institutional framework that AG PH DB 6.42 SOUTH was built on the basis of this TL G IL CL 16.63 “administrative division” of OT MH DJ 14.78 SOUTH-WEST development responsibilities VL GJ 9.48 TM AD 6.75 should take this finding into WEST HD CS 9.32 account. Each regional CJ BH 8.52 development authority has a NORTH-WEST MM SM SJ BN 11.49 significant share of rural BV SB 2.22 CENTRE underdevelopment problem to MS CV HG AB 7.16 cope with. BUCURESTI * Source: LSMS 1996 The aggregation of the development indexes at regional and subregional level has allowed us to distinguish regional patterns in rural development, respectively in rural poverty. At the bottom of development hierarchy are the Botosani-Iasi-Vaslui and Teleorman-Giurgiu-Ialomita-Calarasi (Table 3.5). Poverty variations within one and the same geographic region are the widest in the southern part of the country: rural poverty in the counties situated in plains (Teleorman-Giurgiu-Ialomita-Calarasi) is far more severe than in the hills and the mountains (Arges, Dambovita and Prahova). This is a paradoxical finding as household and community analysis has shown the positive association of arable landholdings with consumption and more general measures of welfare. Human resources, not natural resources may explain the poverty of villages in the plains where the major occupation is farming, a low-income occupation, hence the poor status of households. The contrasting pattern of poor counties situated in the plains and less poor hilly-mountainous counties holds also for the south-west region of Oltenia where the head count index in Olt-Dolj-Mehedinti is higher than in Valcea-Gorj. The lowest community poverty index is recorded in the Brasov-Sibiu and Timis-Arad subregions.
58 3.4. Summary And Conclusions
In the first section, we examined the correlates of household welfare, linking the level of consumption per adult equivalent to the availability of household and community resources. This approach is employed, in section two, to examine the marginal impact of each resource on household welfare. The correlates with the biggest positive impact on household consumption are education, access to formal employment or pensions, and ownership of land and livestock. Factors that impact adversely of household consumption are, in addition to low endowments with human and physical resources, the dependency ratio and gender of the household head. Finally, location variables were confirmed to have a significant effect on household consumption, probably because they enable rural household to market the output they produce or the factors they own. A general conclusion of the first two section is that, given the low level of resources in rural area, the marginal productivity of labor and other resources is extremely low. As pointed in chapter two, serious consolidation in land, livestock and capital should occur to increase this productivity. For such consolidation to occur, exit options away from farming and entrance options into non- agriculture activities – very scarce at present –should be encouraged by appropriate market-based policies.
In section two, we reduced the number of predictors used in the model to four key factors that affect the level of household welfare. We discovered interesting patterns in the empirical association of the household resources that have impact on the choice of rural development policies. Low education, for instance, is a problem for the elderly rural population, but is less important for the rural youth.
In the third section we use a variant of the model developed in the first section to predict the development level of all communes in Romania, so that a poverty map for the whole country can be prepared. This enabled us to detect regional patterns in the development level of rural communities. The less developed rural regions were identified in the southern and eastern part of the country. In the authors’ opinion, rural development efforts should be concentrated in these areas. i Large cattle units are used in the agronomic literature as a way to sum up a diversified livestock, by assigning weights to each type of animal relative to cattle. We used the following weights: tame birds: 4%, sheep and goats 12%, swains 35%, and mules, horses and cattle 100%. ii See Sandu D., “Rural Community Poverty in Romania”, WB, January 1998. iii We used White’s Heteroskedasticity-Consistent Standard Errors & Covariance. iv The functional form of the model of the consumption expenditures is log-non-linear, in which the dependent variable is expressed in natural logarithm, and the significant independent variables are of 5 types: expressed in natural logarithm, in natural units raised at power one, in natural units raised at power 0.5, as binomial of power two, and as dummy variables. Let “a” be the regression coefficient of the independent variable “x”. Then: · If the independent variable is expressed in natural logarithm, then the regression coefficient is equal to the elasticity of y with respect to x. E = a · If the independent variable is expressed in natural units (raised at power one), like “ax”, the value of the elasticity is E = ax · If the independent variable is expressed in natural units raised at power 0.5, like “ax 0.5”, the value of the elasticity is E = ax 0.5/2 · If the independent variable is expressed as a binomial of power two, like “ax + bx 2”, the value of the elasticity is E = ax+2bx
59
· For dummy variables, we computed the percentage change of the mean independent variable corresponding to a change of the dummy variable from 0 to 1, as M% = ea – 1, where “e” is the Euler constant. v The method used reduces the number of predictor to a fewer number of factors, or latent variables, that are able to explain most of the variation in household consumption per adult equivalent. This is called “principal component analysis”. Each of the factors is obtained as a linear combination of the (standardized) predictors. The last column in Table 3.3, communalities, specifies the proportion of the variation of the predictor that is explained by the factors. Weights smaller than 0.2 were omitted from the table (matrix). The KMO coefficient was 0.62 The last row of the table specifies the proportion of the variation in the predictor matrix explained by each of the factors. vi Aggregation is derived by an algorithm that uses the main components applied to the 241 community sample of the Integrated Household Survey. The incidence of poverty (CONSUMPTION) is calculated from LSMS 1996 data against consumption and the poverty line set at 60% of the average consumption per adult equivalent (NCS method). The general fertility rate FERTIL was measured at the 1992 census as the number of live births in thousand women at census-taking time. OUTMIGRATION and INFANT death rates are calculated from NCS data for 1994-96. The index-to-constituent indicator ratio is given by factor score: COMMUNITY POVERTY INDEX = 0.474* In (FERTILE) + 0.436*In (OUTMIGRATION) + 0.308* (sqrt (CONSUMPTION) + 0.253*In (INFANT) where sqrt indicates square root. In the end, the index is multiplied by 10 to make it more readable. The four indicators account for 42.5% of the given matrix variations. If the same data are used to derive the factors by the maximum likelihood approach, X2 = 1,52, which does not differ significantly from 0 for p = 0.56, hence the consistency between the observed and theoretical relationship matrices. vii Community consumption, access to public service and, more generally, regional resources are more accurately estimated by such specific community indicators as infant mortality rate, out- migration rate and general fertility rate (number of live births in thousand women of fertile age - 15- 49 year old). High infant mortality rates, high out-migration rates, high general fertility rates and high head count indices measured as the percentage of individuals whose consumption is below the poverty line increase the chances for communities to be poor. Communities are identified as poor if they are characterized by high infant death rates, high out-migration rates, high birth rates and low aggregate consumption of its constituent households. The aggregation of the above-mentioned four indicators is a measure of community poverty. viii Counties were grouped by “development” sub-region according to a pattern identified in “Regional Disparities in Romania, 1990-94” RAMBOLL Consultancy Group, Bucharest, 1996.
60 4. Findings and Policy Recommendations
In the previous chapter we argued for the need of introducing poverty alleviation concerns into the rural development strategy for Romania. In this last chapter, we develop a rural development strategy with an important poverty alleviation focus.
4.1. From Agricultural Policies To Rural Development Policies
In 1999, Romania’s rural areas still bear the burden of past industrialization policies during the socialist regime, and partial and hesitant pro-market policies experimented in a decade of transition. Three decades of socialist industrialization extracted young and educated rural inhabitants for the urban industries, and transformed the class of peasant farmers into unskilled labour of producer cooperatives. The effects of industrialization and collective farming policies are still visible. The rural population dropped by 20% during 1977-1997 when its ageing was increasingly obvious and the education gap that separated it from urban areas was growing wider. Small and medium-sized enterprises, a major rural employment and income- generation source in market economies, were either brought into the cooperative sector, or eliminated. As it was typical for the socialist economy, industrialization - synonymous with large-scale production – or most of the industrial investment was concentrated in urban areas, leaving rural areas almost mono-occupational. Even in 1999, ten years after those policies were discontinued, rural areas have hardly any employment alternatives to agriculture to provide.
At the outset of the transition, rural development policies and agricultural policies were virtually one and the same thing, as support for agriculture was – wrongly – equated with rural development. Many of the agricultural policy actions of the time were aimed to rescue the former state-owned agricultural sector, which was ill equipped to meet market requirements. Unfortunately, significant budget resources were wasted in the process that resulted in a deteriorating macroeconomic performance and delayed rural development. Paradoxically, until 1997 concerns to support small farmers, the most important producer segment in rural area, were marginal. Currently rural development policies are yet to be defined. Since the rural area was decisively affected by agricultural policies in the early years of transition, we will briefly outline in this section their achievements and shortfalls in terms of rural development.
The year 1989 marked an end to central planning in Romania and the beginning of transition to a market economy as a period of economic searches and attempts with a strong social impact which is ongoing. The most dramatic structural change that shaped rural Romania at the start of transition was the land reform. Between 1989 to 1990, farmer cooperatives were spontaneously dismantled by former landowners. That “repossession” was ratified by law in 1991 when the rules of the game were set: a) restitution in kind, placing a 10 hectare limit in arable equivalent on the amount of land that could be restituted to a household; and b) the possibility of landless cooperative members, local public servants or households willing to settle down as farmers to receive land in the event of a farmland surplus. Most of the agricultural land was restored to its former owners who were forced to join the producer cooperatives during 1948-1962, or to their heirs. By design, the land reform transferred most
61 of the land (two thirds) to elderly farmers, its former owners forced to associate into cooperatives between 1948 and 1962, and very little to the rural youth. As most of the former landowners who had died had several children who claimed ownership rights, at the end of the reform the land ownership structure was more fragmented than before the start of process of collectivization in 1948, to reach unprecedented levels in Romania. In 1992, more than four million landowners with holdings averaging two hectares divided in several plots had taken the place of the five thousand or so producer cooperatives. The private small-scale farm sector that managed eight million hectares of farmland was the main rural sector in terms of land ownership, employment, income-generation and contribution to gross domestic product.
In addition to land, producer cooperatives also had other physical assets, mainly livestock, buildings and equipment. Of these, livestock was returned where possible to their former owners. The buildings and such little farm equipment as was acquired after collectivization were either assigned to farm associations in their capacity as successors to cooperatives or sold at auction. Part of the buildings was simply demolished and the construction materials distributed to ex-members. Very little mechanical equipment was inherited from producer cooperatives. Before 1989, mechanical equipment such as tractors or harvesters was owned by specialized service providers who eventually changed their names to Agromecs or Servagromecs. Just like one-time cooperatives, the new landowner class depended on the mechanical services provided by these units. Almost ten years after the land reform, private farm operators are still poorly equipped with farm machinery.
The land reform changed dramatically the way agriculture was organized. The former arrangements for input procurement, output marketing and credit mechanisms became unsuitable for a numerous and dispersed clientele. Land fragmentation increased transaction costs in all market channels, for inputs, factors or outputs. In the short run, the use of inputs, machinery services and credit dropped severely. The new class of farmers, lacking modern agricultural knowledge and facing severe cost disadvantages due to their small-scale operations, switched their production mix away from modern toward traditional crops / technologies, and reduced their market transactions to a minimum in favor of an autarchic production system. A dual agricultural system emerged, with state farms and some private association producing for markets, and the private small-farm agriculture producing for own consumption. A severe drought added to the problems created by the new organization of agriculture, resulting in a large fall in crop output in 1992.
The former way of thinking was still dominant. The output drop in 1992 was interpreted as a signal that market forces do not work in agriculture, and instead of increasing the pace of output and factor market liberalization, rural areas were abandoned to “the heavy hand of the state”. The private small-farm sector was placed into a straightjacket consisting of parastatals with monopoly role in input procurement, and monopsony role in output “collection”. While in the rest of the economy prices were freely determined, major agricultural commodity chains in meat, milk and wheat were subject to pan-seasonal and pan-territorial pricing rules just like in the time of central planning. To guarantee observance of these prices, major distortions were introduced in the relative prices of agricultural commodities and factors. “Compliant” farmers benefited from subsidies in the form of “premia”, inputs at lower prices and preferential credit. Such subsidies were delivered through state-controlled channels, the so- called “integrators”, creating major competitive disadvantages to traders outside the system, private traders more particularly. As in most “socialist-like” allocation mechanisms, this one
62 was based on a combination of fixed prices and queuing. The demand of state-mandated integrators was administratively decided by the former planners, and backed up with preferential credit for and procurement orders from integrators. This demand cleared the supply of the state-owned farms, some of the supply of the associations and little from the private, small-scale farmers. While the regulations provided access to the system to all, the integrators opted for cost-effectiveness and preferred large-size low-cost transactions to those involving small farmers. In this way, a non-competitive, hence inefficient, system was created which depended on subsidies and widened the gap between commercial agriculture and the autarchic small farmer sector.
The “heavy hand of the state” policies repressed rural factor markets heavily, notably the land market. From 1991 to 1998, the restituted land was banned from sale, thus hindering a market-led land consolidation. Mistrusting market forces, the authorities considered for eight years a dead-end alternative, the creation of a state agency that should buy land, consolidate it and sell it in compact plots, despite the international evidence that such agencies have invariably ended in failure. The repressed land sale market maintained artificially the mismatch between access to land and access to labor at household level, and prevented the infusion of young labor into agriculture. Finally, this measure contributed to the perpetuation of high transaction costs in agriculture, and the development of second-best, less secure arrangements such as producer association, sharecropping or land leasing. Only in 1994 were cumbersome land leasing regulations adopted, and four more years passed before they were made simpler.
Another factor market severely repressed by inappropriate policies was rural credit with its adverse microeconomic effects. On the one hand, agricultural credit was rationed by fiscal and quasi-fiscal mechanisms, for the benefit of large, especially state-owned, farms that incurred high taxpayer and consumer costs. Rural non-farm operators could borrow only at market interest rates. Such policies discriminated heavily against small farmers, self-employed and non-agricultural rural entrepreneurs. It also had negative effects on investment and output among these groups that were queuing for preferential credit and postponed year after year their investment plans waiting for such credit to reach them. In addition to postponing growth, any commercial credit available was collateral-based, a technology which denied access to bank loans, either concessional or at market interest rates, to most of the creditworthy rural borrowers – such as farmers or informal non-agricultural entrepreneurs. Furthermore, the credit was expensive, provided at high real interest rates, to cover high-risk premiums and offset the burden of a bad lending portfolio. For most of the rural entrepreneurs, the only credit alternative was from suppliers or customers, a very expensive source. The segmentation of rural credit markets imposed high penalties on creditworthy borrowers, transferring through economy-wide policies the benefits of concessional agricultural credit to a few inefficient state-owned farms.
Preferential agricultural credit policies also had major macroeconomic adverse effects. Preferential credit was heavily subsidized and the cost of subsidies was paid by millions of taxpayers. Since more subsidies were needed than the state budget could provide, cheap money was printed for the benefit of the same beneficiaries. Many state-owned enterprises, inefficient and mismanaged, did not meet the creditworthiness standards of commercial banks. In an effort to help a “good cause”, both The Executive and Parliament “persuaded” the banks to lend by providing explicit or implicit guarantees to preferential credit. Alas, the results were
63 below expectations. Even though credit was very cheap, users failed to operate at a profit or repay the loans. Most of the loans were not paid back when they fell due. Adding to the planned subsidization, the budget had to honor the explicit guarantees for bad loans. Furthermore, the losses of large state-owned farms and banks had to be borne by taxpayers, in the last resort. When quasi-fiscal liabilities became due, the state budget came under pressure.
Soft agricultural credit policies introduced political instead of creditworthiness allocation criteria instead of creditworthiness criteria. The result of these policies that led to a fairly low output and large money supply was inflation, a hidden crude tax paid by all. Furthermore, as the loan portfolio of commercial banks deteriorated, credit at market interest rates became more expensive. Bank intermediation margins were raised in an attempt to recover from creditworthy borrowers the losses incurred by mismanaged enterprises. Interest increased to high positive real rates to offset not only high-risk costs, but also bad loan portfolios. As a result of the high interest rates, investment and therefore, economic growth, contracted. Most Romanians had no choice but to tighten their belts.
The “heavy hand of the state” policies also prevented the development of private, de- centralized input and output markets. The “integrator-type” policies gave the monopoly of subsidized provision of inputs to state-owned parastatals, blocking private sector competition. In most of the cases, these parastatals consumed part of the subsidies to cover their inefficiencies. Similarly, on the output side, pan-seasonal and pan-territorial policies had the same effect.
Not only agricultural policy mistakes took their toll on the rural economy. It also stood to lose from inconsistent, hesitant macroeconomic policies. So, for instance, an over-appreciation of the national currency in an attempt to keep inflation under control was costly to the sectors providing tradable commodities such as agriculture, leading to lower prices of imports on the local market and lower returns on farm exports. In the field of structural adjustment policies, the slow pace of privatization resulted in a steady deterioration of the performance of state- owned units, in the absence of effective owner’s control. A large state-owned enterprise sector which does not pursue profit maximization also had a damaging effect on the competitive environment which is key to working markets. Also related to structural policies, slow privatization in the banking sector coupled with weak supervision by the central bank and poor management ended up in a bad loan portfolio and bad customers. Two of Romania’s largest commercial banks had to be rescued from the failure they were doomed to. All those mistakes contributed to the contraction of the economic activity.
Those policies were partly visible to the naked eye in 1997. Integrators accumulated losses, a large part of the preferential credit guaranteed by the state was not repaid, fiscal liabilities were too large to be covered by shrinking state budgets. In the authors’ opinion, such costs were only a fraction of the full economic cost of the “heavy hand of the state” policies. It was the direct costs that were visible in 1997, which called for an end to such policies. Left with huge fiscal and quasi-fiscal deficits in late 1996, the Administration had no choice but to turn to a market-led agricultural reform.
In 1997 the Administration began the implementation of an agricultural reform package, supported by an Agricultural Sector Adjustment Loan from the World Bank. A first pillar of the reform package was to create a “level playing field” for all farmers in Romania,
64 implemented in the first half of 1997. To achieve those goals, subsidies like premia were discontinued and commodity prices were liberalized, trade protection was gradually reduced to moderate levels, the import of certified seeds was simplified and exports were liberalized. Instead of commodity-specific subsidies, an input voucher system was implemented starting the fall of 1997. The volume of preferential credit was reduced, and the disbursement mechanism changed to stimulate repayment. Free land transactions was the second pillar of reform: the ban on land sales was removed and land lease was simplified by August 1998. The third pillar of the reform package included the privatization, restructuring or liquidation of state-owned firms in agriculture, as well as those downstream (grain storage) or upstream (mechanical services, certified seed production). Designed to be implemented until June 1998, the privatization component lagged far behind in mid-1999.
Most of the agricultural reforms started in 1997 had as a major concern the macro-economic imbalances created by the state-owned agricultural sector. Much of the emphasis of reform was placed on “creating a level-playing field” or privatizing state concerns in agriculture. That was only natural, given the adverse effects of prior agricultural policies. However, we must note that rural development actions or support to small farmers were not prominent in the 1997 agricultural reform package.
Some of the measures were designed for the benefit of small farmers. High among them were a) removing the ban on land transactions, b) re-directing subsidies to the private sector via input vouchers, and c) changing the role of the Ministry of Agriculture into a development agency for the private sector. The liberalization of land transactions facilitated the re- assignment of full property rights, opening the door for a market-led land consolidation. The input voucher scheme had three main purposes. First, to boost the demand for cash input of almost autarchic farmers, invigorating weak rural input markets. Second, to include a poverty alleviation component, at the expense of purely agricultural concerns. Third, to re-distribute budgetary funding by re-directing it from a few state-owned enterprises to small farmers. Two 1998 surveys show those goals were achieved. Last but not least, changing the role of the Ministry of Agriculture, previously almost exclusively designed to support and control state- owned agriculture, into a development agency providing public goods for the whole agricultural sector, proved to be an important but time-consuming task that is ongoing.
There are two reasons for having dealt with agricultural policies at some length. The first is that because they were largely the cause for rural underdevelopment and may now take the rural inhabitants from poverty to development. The second is that we wanted to underscore how important agricultural and macroeconomic policies are in the promotion of free entrepreneurship for rural development as a whole, in sharp contrast to the interventionist policies pursued in the recent past, whose negative effects were emphasized herein.
4.2. Characteristics Of Rural Poverty
Today, most rural areas are poor and a good part of the rural population lives in poverty. Four rural inhabitants out of ten were poor in 1998, and the negative growth prospects for 1999 would not change the figure for the better. Among the causes of rural poverty, lack of remunerative employment opportunities stand out as the core reason. On the one hand, formal employment – largely state-owned – became scarcer and scarcer, as enterprise reform was
65 making headway. One the other hand, informal employment – agricultural or non-agricultural – is built on scarce resources that cannot generate enough income to shield from poverty those depending on it for a living. Farmers, the largest group of informal entrepreneurs in rural areas, are involved in a last-resort occupation, which signals the lack of alternative employment.
The only large group of rural inhabitants that stand a lower poverty risk is the pensioners. They are so because they can complement their pension benefits with the agricultural income from land ownership. About 40% of the rural inhabitants live in pensioner headed households which account for about two thirds of private land ownership. They were the winners of the 1991 land reform. Part of the land they own is leased out, mostly to agricultural companies. It seems that elderly farmers have access to more land than labor.
In rural area, almost every household category holds land (84% of the rural population) as a result of the 1991 land reform. Apart from ethical aspects, such as restitution, or those purely agricultural, the main achievement of the land reform is its contribution to poverty alleviation in rural area. Most of the rural inhabitants became landowners and are working at least a part of the land they own. The amount of land per household was and is extremely small, but in an area where earning opportunities are so scarce, an agricultural income makes the difference between living and starving. Agriculture plays an important role as a source of food security and protection against extreme poverty for those who lack a salaried income or social security pension benefit. Such a role is consistent with a production pattern that favors output diversification (to reduce production risks), traditional production patterns (low level of cash inputs), and the use of output mainly for subsistence (due to large transaction costs and output marketing margins). As documented in Chapter Two, on-farm use contributes between 30% and 60% to total household consumption, and keeps most of the rural inhabitants out of extreme poverty. However, the amount of land that the average rural household owns is insufficient to shield it against poverty, or to warrant it growth and prosperity.
Most of the rural inhabitants that depend on land for their living are poor. Faced with no earning alternatives, the working-age population has access to small landholdings for both crop and livestock farming. Consequently, marginal productivity is close to zero, as documented by the estimates for specific commodities (in Chapter Two) or for the overall household consumption (in Chapter Three). This means that a significant amount of rural labor can exit agriculture and yet the output will not fall. Agriculture acted - and still does - as an employment buffer, not as an engine for growth. As in other countries of the world, poor agrarian communities are trapped into a vicious circle that prevents them from reaping the fruits of development. Being poor, households choose low-risk production strategies, based on a traditional crop and livestock mix, as well as traditional technologies. In turn, this production behavior leads to low returns. This low-risk low-return strategy is detrimental, however, to agricultural growth. It perpetuates the duality of Romanian agriculture, where the interactions between commercial and household agriculture are minimal. Such low-risk low-return strategy perpetuates rural poverty.
There are little employment opportunities besides farming in rural Romania. In the ‘90s, the underdevelopment of non-agricultural activities in rural area was compounded by inadequate sectoral policies, agricultural in particular. Measures such as pan-seasonal and pan-territorial price setting postponed the development of decentralized, private and efficient input
66 procurement and output marketing channels. Such policies that lasted from 1992 to the beginning of 1997 hindered private sector entry to sectors downstream and upstream farming. In addition, the large share of autarchic farmers put a brake on market development, as a result of depressed demand for inputs and factors, and low supply of agricultural commodities. Together, these circumstances kept in place a mono-occupational structure in rural area. Wheat and milk are produced and livestock is raised in the countryside: but it is in urban area that flour is made, milk processed, and meat slaughtered and packaged. Rural occupations are seasonal and add little value. More value is added by urban-based occupations and the revenue they generate is used to support urban businesses. As the activities upstream and downstream agriculture moved to urban area, rural markets grew weak. The absence of non-agricultural occupations in rural area brought agriculture-industry-trade multiplier effects to a minimum.
In rural area, the non-poor includes those in formal employment or retired therefrom, or owners of over three hectares of land. Past policies resulted in a division of rural population in two distinct categories: those who worked for the state-owned enterprises (including rural commuters), and the rest. The former category, including wage-earners and social security pensioners, is less exposed to poverty. Land reform coupled with the recent adjustment of agricultural pension benefits and voucher distribution, succeeded to reduce the poverty risk of agricultural pensioners. In contrast, the poverty rate is very high among the rural self- employed, whether agricultural or non-agricultural. The sharp fall in formal employment, mostly urban-based, blocked rural commuters’ access to urban jobs, due to a selective process of firing landowners first. That fact forced agriculture to play the role of an employment buffer by absorbing the excess labor, which resulted in widespread under-employment and low marginal productivity. However, we signaled that rural employees, largely employed by state- owned firms, were likely to suffer more than their urban colleagues from the enterprise reform package. Part of them were expected to join the unemployed, and all of those employed by the state stood to lose the rents associated with a weak corporate governance. However, such costs cannot be avoided if rural development is to be sustainable. Privatization of the state enterprises is the only solution. To note that the enterprise reform will put more pressure on the rural labor market in the short run.
One “positive” aspect about rural poverty is that it is rather shallow in Romania. A large proportion of the rural population clusters around the poverty line, either below or above it. This feature highlights the prime importance of economic growth as the main tool for poverty alleviation. Recent swings in GDP showed that its movement is able to push/pull millions of people in or out poverty. For most of the rural poor, the resumption of growth is the major poverty alleviation tool.
Among the growth-strategies, diversification of economic activities is very important. However, the current role of non-agricultural entrepreneurs as a source of employment and income-generation in rural area is still limited. If one considers the income level these activities are generating, they seem to develop out of the need for survival, rather than perceived profit-making opportunities.
In the short run, agriculture may became the one important tool for a growth-oriented strategy, especially because its large share in rural employment. However, agriculture suffers from frozen factor markets, especially land. The sector has yet to recover from the dramatic structural effects of the land reform. Landholding is highly fragmented, and there is a severe
67 mismatch between access to land, labor and capital at the household level. Unlike the elderly, young farmers have access to too little land. In addition, productive equipment is scarce, obsolete and hardly suitable for small-scale farming. Input and output channels, as well as the provision of extension services and credit, are still inadequate for the small-scale farmers.
A rural development strategy should address the problem simultaneously. First, part of the land owned by elderly farmers should be transferred, through various market mechanisms such as leasing, sharecropping or selling, to younger farmers. Second, part of those employed in farming today should leave agriculture for non-agricultural activities in rural areas. In this way, the labor to land ratio in farming can improve and marginal productivity rise to make agricultural income provide enough for a decent livelihood in rural area.
However, for this scenario to work, two conditions must be met. First, that elderly farmer may exit farming, most probably in return for adequate financial returns and a social safety net. Second, that working-age farmer whose marginal productivity is zero or near-zero today can find alternative employment in rural area, apt to generate a decent income. This scenario hinges on the resumption of economic growth and the implementation of a rural development programme where legal and economic policy provisions are concerted and financially supported.
How fast can this process be put in place and the work force in agriculture become slimmer? The answer depends on the intensity of rural development actions to be taken. We have already said that a stable macroeconomic framework and sectoral policies that provide an even playing field to all activities and players is a precondition for sustainable rural development. In the absence of other direct support actions, unbiased policies that would put in place the conditions for rural development may take one or two decades to yield fruit. The main cause for this pessimistic forecast is the hard-to-break vicious circle of rural poverty. Poor rural households will continue to stay away from input, factor or output markets. Both a cause and an effect, transaction costs for this large segment of rural self-employed, owner of about 60% of the land resources, would remain large. Market forces and arbitration, the essence of economic gains, as well as specialization, the old principle that leads to higher returns, will not emerge in rural areas any time soon. In order to break the deadlock, concerted action is required to be backed by adequate policies, regulations, enforcement capacity and financial resources to challenge it. A rural development strategy is required.
4.3. From Rural Poverty To Rural Development
The diagnosis outlined in the previous chapters is the basis for a rural development strategy we suggest herein. Its underpinnings are:
Ø Fast restructuring of the private small-farmer sector through:
¨ Measures to stimulate the land market and farmland consolidation
¨ Measures to stimulate farmer associations (land pools) for input supply and output marketing
68 ¨ Provision of public goods for the benefit of small farmers
Ø Diversification of the rural economic base, through the development of non-agricultural activities; and
Ø Human capital development.
4.3.1. Restructuring The Small-Farmer Sector
Our first recommendation concerns the relationship between rural development policies and agricultural policies. Rural development policies should not be one-faceted, agricultural-only, like in the past. Nowhere is the detrimental effect of channeling resources to one sector (e.g. agricultural concessional credit) better illustrated than in Romania. However, agricultural policies should be a major component of rural development policies given the structural characteristics of rural area where agriculture and the sectors upstream or downstream cut the largest share of rural employment. In turn, agricultural development should pursue the development of private small-scale farming as small farmers, owners and operators account for roughly 60% of Romanian agricultural landholdings. They are the future of Romanian agriculture.
The agricultural policy component of the rural development strategy should include three kinds of measures:
Measures to stimulate working land markets and farmland consolidation. A stronger small farmer sector should solve two problems:
Ø Economic: the mismatch between access to land and access to labor at rural household level
Ø Technical: the transfer of land from owner to another owner, to lessor or to sharecropper.
Realization of the sector’s potential will depend on the speed of the process of land consolidation and entry of younger farmers.
Let us consider the economic side first. Both land consolidation and young labor entry to the sector depend on chances of those currently employed in it to exit. This is all the more an issue today when agriculture acts as an employment buffer, and is burdened by severe underemployment. Such exit possibilities depend on two factors: a) the rate of development of non-agricultural business in rural areas, and b) the possibility that elderly could assign their land ownership rights in return for financial revenues that would guarantee them a decent living.
A variety of measures are required to solve the mismatch between access to land and access to labor in agriculture. Such measures should facilitate the transfer of land from old people and non-rural residents to younger rural households. There are several options the Government could contemplate:
69 Ø Introducing a support scheme (voucher) for young landless or near-landless households that are willing to buy land similar with the voucher scheme for inputs. The aim is to stimulate both farmland consolidation and poverty alleviation. The Land owners who want to consolidate their farmland by buying land in compact plots could benefit from a lump sum (fixed subsidy) per hectare, eventually differentiated by region and type of land, given up to a ceiling of 10 ha. Such a scheme would: a) be relatively equitable channeling the state support towards the small farmers with limited investment capacity; b) would stimulate the land market, a relatively young and underdeveloped market; and c) would not distort farmers’ decision to produce certain outputs and use certain inputs, as commodity specific subsidies do.
The positive impact of farmland consolidation on productivity, documented in chapter 2, provides its own justification. From a social point of view, focusing on small farmland owners ensure -–given the strong correlation between land ownership and household welfare – a better targeting of resources towards the most vulnerable groups.
Ø The provision of free extension services to facilitate land switches, aiming to consolidate small farms in compact plots.
Ø Implementation of agriculture income tax, maintaining the actual presumptive method of determining the fiscal burden. The high level of informal activities in agriculture requires maintaining the presumptive method. Apart from budget revenue considerations, such a tax would ensure that landholding is not just a saving instrument and facilitate land transactions.
The impact of the above measures largely depends on macroeconomic performance. A positive if limited impact is expected in a contracting economy but a stronger effect is foreseeable if economic growth is resumed.
The technical aspect that concerns the transfer of land operation to other persons is as important as the former one. The liberalization of the land market and simplification of land lease provisions in 1998 paved the way for a market-led farmland consolidation. Both kinds of land transactions are still at an early stage. We have found that the most frequent mechanisms for the transfer of land surplus are by holding stakes in landholding companies and sharecropping.
For a working land market to develop, much remains to be done by the central and local governments. The first step was taken in 1996 when investment in a national land cadastre was initiated. The system which is to be completed in about ten years’ time, will provide information to buyers and lessees on the owner of the arable plot of land, the claims on it and its characteristics. Apart from the agricultural cadastre database, there is very useful information the provision of which may be started right away. Of that information, dissemination of the land transaction data may quickly help a better working land market. Such data include the selling price, the per hectare rate of the land leased, or the percentage of the crop in sharecropping, by land class, quality and location. Local agricultural administrations may provide jointly with the mayors’ offices this information freely, for the benefit of agricultural landowners, since such information is in the nature of public goods.
70 Also, its dissemination by the mass media should also be supported, at least by providing it free of charge.
Many instances were reported of fraud and deceit associated to land transactions. They were partly favored by the use of informal land lease arrangement, such as sharecropping which are not protected by specific laws. Part of them were due to contractual indiscipline. To minimize these cases the legal framework needs improvement to a) provide legal cover to widely used informal transactions; b) better protect the parties (seller-buyer, lessor-lessee, etc.) to land transactions; c) streamline registration procedures; d) introduce quick enforcement means to recover the loss incurred by breaches of the contract; e) guarantee third parties access to some information about transactions. All these aspects should be dealt with by a working group including legal and agricultural experts.
Measures stimulating farm input procurement and output marketing associations. Such farmer associations with input supply or output marketing as their aim are an important part of the rural development strategy. Currently they are hardly represented in the rural area. To stimulate their emergence and development, The Executive may consider granting them not- for-profit taxation status. And in order that Romania’s agricultural policy-making framework be brought in line with that of the EU, the dialogue between Government and professional and interprofessional associations should be institutionalized and strengthened.
Promotion of activities in the nature of public goods for the benefit of small farmers. We include herein a number of services that the public administration, viz. the Ministry of Agriculture and the public institutions under it, should provide to small individual farmers: agricultural advice, applied research, dissemination of information on the prices of major agricultural commodities, legal advice on land transactions to landowners, quality standards and certification of agricultural commodities.
4.3.2. Diversification Of The Rural Economy
Our analysis emphasized the lack of non-agricultural employment opportunities in rural area. To support the growth of non-agricultural activities, Government needs to develop a coherent set of policies to promote local private activities downstream and upstream agriculture, such as agro-processing, input and mechanical service provision, etc. This would require an adequate macro-economic framework, and comprehensive support in term of credit, training and advisory services, facilitation of access to information on technologies, marketing etc., and simplification of administrative and bureaucratic procedures.
As most of these new rural ventures would be, at least in their infancy, small businesses, such policies should be a component of the small business development policy in Romania. The set of interventions may include:
Ø Installation grants or investment grants, for rural entrepreneurs starting or developing business in (possibly selected) rural areas. Our advice is that such a system be modeled to the input voucher scheme rather than provide subsidies credit. Credit subsidization provides more subsidies to users who have access to a larger amount of credit. A simple system with subsidies being proportional with job creation may be more efficient and cost-
71 effective. Besides, such a system does not discriminate among business of different sizes or from different sectors. We also suggest that such grants be distributed by a streamlined procedure that would require job creation in rural area rather than provide too many eligibility criteria Wherefore we suggest that sectoral eligibility criteria be dropped, to give the entrepreneurs full control over their decision-making as to the activities they want to start.
Ø Subsidization of start-up activities provided by the small business development centers or business incubators for the benefit of rural entrepreneurs. In case such not-for-profit centers are established in rural areas, subsidization of business establishment costs may be considered.
Business creation and development largely depends on the availability of credit. The recently adopted framework for secured transactions (a more comprehensive form of commercial pledge) is a move in this direction. It introduces a modern system of creation, registration and enforcement of security interests in movable assets. The new system that Government intends to enact this year will be simple, transparent, low-cost to users and quick in enforcing repossession. It will boost small and medium size businesses, including in rural areas, by giving access to credit secured by movable assets to a class of users that are currently left out by traditional credit technologies. We expect the new system to increase both bank lending and credit provided by traders, merchants and input suppliers. This will in turn stimulate investment and economic growth.
Development of modern lending mechanism is essential for a rural economy where capital is in scarce supply. Such mechanisms whose role is outstanding in market economies but which are little used in Romania are warehouse receipts and equipment leasing arrangements. In the former case, public licensing and inspection institutions should be set up to certify the “bona fide” operation of warehouse operators (grain silos) in partnership with private-sector business associations. The development of a warehouse receipts system in Romania will simplify inventory financing in downstream industries (storage, milling, oilseed crushing etc.), all while increasing the size of liquid assets of these firms, hence demand for small farmers’ output. It will also increase competition among storage operators and lower storage fees, thereby raising demand for such services. Lower storage fees will induce farmers to keep their grains in silos, reducing the currently high storage losses as a result of inadequate storage in barns, lofts or cellars. As to equipment leasing, a level playing field for domestic and cross-border leasing is needed: the current taxation and depreciation system discriminates against domestic equipment and puts the domestic transport equipment industry at a disadvantage.
The rural area can hardly enjoy the benefits of the economies of scale specific for urban area/ Typically rural businesses use to advantage local resources to make products that are marketed in town. Rural-based businesses will be promoted when supply and marketing costs from and to urban area are lowered. Public policies to bring transaction costs down, such as development of public infrastructure for roads, communications and water distribution, should be promoted.
Finally, a sustainable private sector development should go hand in hand with a fast pace of privatization of state-owned enterprises in rural area.
72
4.3.3. Development Of Human Capital In Rural Area
There is a wide educational gap between rural and urban areas. All over the world there is such a gap and it is doubtful that it will be bridged in Romania. We have already shown that better education attainment is an important ingredient of poverty alleviation policies. In order to take advantage of the new economic opportunities afforded by the transition to a market economy, new skills are required. The following pro-active measures may help the acquisition of such new skills:
Ø Continuous vocational education that existed during the centrally planned regime should be promoted in rural areas to improve adult education in general, as well as to promote opportunities for professional and vocational specialization and upgrading.
Ø Training in farm management, and farm technologies. Subsidization of books on these subjects may be an efficient way of doing this (self-targeted subsidy).
Ø Access to the rural self-employed to retraining courses designed for the unemployed. While the current unemployment provisions disqualify landowners from unemployment benefits, they should not discriminate against rural when it comes to their participation in pro-active labor market activities, such as vocational training or retraining.
Ø Public works to improve local infrastructure and generate local employment (especially those that are labor intensive)
4.4. The Regional Dimension Of Rural Development
A methodology has been provided herein for the identification of less developed rural areas, on the basis of primary household consumption data, and tested their stability over time. As rural poverty proved to be a socio-economic phenomenon well defined geographically and persistent over time, the geographical location may be used for regional targeting, with a fairly reasonable amount of leakage.
While rural underdevelopment is extensive, the regional dimension of rural poverty has been documented by our analysis. In Chapter Three, we identified the rural areas where the poverty rate is high, living standards are low, housing is almost primitive and public services are poor. Such findings make authors suggest that rural development policies should be targeted to these places. Annex 4 lists the communities in the bottom quintile.
73 Annex 2 Statistical Tables
86 Table A1.1 Rural Housing Indicators, 1997 Rooms per Living space per Living space per Housing units in % private ownership housing unit housing unit inhabitant 1000 inhabitants RURAL 2.6 34.0 12.3 362.3 97.4 Alba 2.2 34.4 12.7 367.8 96.3 Arad 2.6 41.4 15.6 376.6 95.0 Arges 2.5 30.2 12.0 397.7 98.4 Bacau 2.5 33.9 11.6 341.4 98.2 Bihor 2.3 35.8 13.3 370.9 98.4 Bistrita-Nasaud 2.3 39.1 11.9 303.8 96.6 Botosani 2.2 27.1 10.4 383.1 99.0 Brasov 2.4 38.8 13.7 352.6 91.2 Braila 3.2 35.8 12.7 354.7 96.7 Buzau 2.9 32.8 12.8 390.9 98.5 Caras-Severin 2.6 39.1 14.3 364.8 93.4 Calarasi 3.1 33.1 11.5 347.2 97.6 Cluj 2.1 33.7 14.1 416.7 98.1 Constanta 3.2 37.7 11.5 305.8 94.9 Covasna 2.0 33.5 12.9 384.4 97.1 Dîmbovita 2.7 32.3 11.2 344.9 99.1 Dolj 3.0 35.1 13.8 392.7 98.9 Galati 3.1 37.5 12.7 337.3 98.0 Giurgiu 3.2 34.6 13.2 380.5 99.0 Gorj 2.7 31.3 12.2 391.8 97.2 Harghita 2.0 34.6 13.2 381.8 98.5 Hunedoara 2.2 34.6 13.9 402.5 92.3 Ialomita 2.9 32.2 11.5 358.8 97.5 Iasi 2.4 29.7 9.3 314.6 98.7 Ilfov 3.1 38.6 12.8 330.6 94.0 Maramures 2.2 35.4 11.3 319.7 98.0 Mehedinti 2.6 31.9 14.0 440.1 96.5 Mures 2.2 34.8 12.8 368.2 96.9 Neamt 2.5 32.8 11.0 336.4 97.9 Olt 2.8 31.2 11.7 375.0 99.5 Prahova 2.9 34.5 11.9 344.8 98.4 Satu-mare 2.2 35.8 12.1 338.8 97.7 Salaj 2.2 36.1 15.0 416.2 98.9 Sibiu 2.4 39.7 15.1 379.6 93.8 Suceava 2.3 33.8 10.7 315.0 97.2 Teleorman 2.8 30.1 11.5 381.4 99.7 Timis 2.5 41.6 14.9 359.6 91.4 Tulcea 3.3 37.6 13.9 370.0 95.2 Vaslui 2.3 28.7 10.6 367.8 98.7 Vâlcea 2.5 28.6 11.8 410.9 98.8 Vrancea 2.8 35.1 13.1 373.9 98.8
87 Table A1.2. Physical infrastructure, by urban/rural area Total crude length of drinking water pipelines - km Total crude length of sewers - km Length of gas distribution pipelines - km Total Rural %/ total Total Rural % of total Total Rural % of total Total 35,287 13,551 38.4 15,502 865 5.6 18,017 5,591 31.0 ALBA 761 283 37.2 287 2 0.8 901 350 38.9 ARAD 1,153 424 36.8 435 27 6.3 408 121 29.6 ARGES 1,518 711 46.8 624 30 4.8 385 88 22.9 BACAU 592 44 7.4 416 16 3.9 501 92 18.3 BIHOR 1,249 533 42.7 524 46 8.7 94 23 24.7 BISTRITA-NASAUD 480 154 32.2 225 14 6.4 367 230 62.7 BOTOSANI 641 239 37.3 224 27 12.0 98 8 7.9 BRASOV 987 169 17.1 546 11 2.0 975 214 22.0 BRAILA 1,133 427 37.7 264 3 1.1 150 19 12.6 BUZAU 969 659 68.0 165 8 4.7 273 82 30.2 CARAS-SEVERIN 548 95 17.4 274 12 4.3 329 52 15.9 CALARASI 620 305 49.1 139 3 2.2 - - - CLUJ 1,713 959 56.0 570 57 9.9 1,212 457 37.7 CONSTANTA 1,469 411 28.0 1,009 35 3.5 10 - - COVASNA 286 94 33.1 131 10 7.5 146 19 13.3 DAMBOVITA 546 338 61.9 151 14 9.0 583 261 44.8 DOLJ 703 25 3.5 503 11 2.2 352 4 1.2 GALATI 871 233 26.8 616 29 4.7 242 - - GIURGIU 147 23 15.8 93 3 3.2 19 - - GORJ 636 240 37.7 175 22 12.5 367 117 31.9 HARGHITA 801 429 53.6 208 - - 221 55 25.0 HUNEDOARA 862 115 13.3 630 38 6.1 526 111 21.1 IALOMITA 618 316 51.2 148 20 13.1 77 - - IASI 747 99 13.2 493 36 7.4 390 63 16.0 ILFOV 51 29 58.1 73 61 83.8 61 32 53.0 MARAMURES 1,677 1,170 69.8 322 29 9.1 599 218 36.4 MEHEDINTI 411 198 48.1 147 8 5.3 - - - MURES 712 153 21.5 532 69 12.9 2,551 1,874 73.5 NEAMT 596 244 40.9 303 22 7.1 194 30 15.6
88 OLT 550 266 48.3 185 2 0.9 142 29 20.3 PRAHOVA 2,072 851 41.1 490 44 9.0 1,393 483 34.6 SATU-MARE 453 205 45.2 248 14 5.5 224 21 9.5 SALAJ 625 447 71.5 143 5 3.2 118 29 24.2 SIBIU 592 77 13.0 389 14 3.6 1,015 420 41.4 SUCEAVA 676 184 27.1 529 21 3.9 144 27 18.4 TELEORMAN 420 85 20.2 221 23 10.2 - - - TIMIS 1,598 802 50.3 530 20 3.8 463 16 3.5 TULCEA 860 566 65.8 92 5 5.1 - - - VASLUI 601 207 34.5 350 11 3.0 88 - - VALCEA 572 246 42.9 276 36 13.1 221 5 20.3 VRANCEA 776 497 64.1 160 11 6.6 91 - - Bucuresti 1,999 - - 1,664 - - 2,091 - -
89 Table A1.3 Predictors of human capital development Dependent variables in regression models Classrooms in 1000 Teaching staff in 1000 Physicians in 1000 inhabitants inhabitants (conversion inhabitants (regression by by square rooting) (conversion by square square rooting) rooting) Constanta 0.941* 3.67* 0.46* Live births in 1000 women alive, of fertile age in 1992 0.0003* 0.0003* -0.0001* Share of agricultural population in total working-age -0.01* -0.02* -0.01* population, 1992 (conversion by square rooting) Share of population aged 60+ (logarithmic conversion) 0.36* -0.20* 0.05* Share of arable land in total farmland -0.04* -0.02 0.004 (conversion by square rooting) Distance to nearest town of over 30 thousand inhabitants 0.04* 0.04* -0.02** (logarithmic conversion) Population of nearest town of over 30 thousand inhabitants -0.01 -0.02 0.04* (logarithmic conversion) Located in Moldova (1 yes, 0 no) -0.09** -0.009 -0.04 Located in Muntenia (1 yes, 0 no) -0.25* -0.15* -0.001 Located in Oltenia (1 yes, 0 no) -0.04 -0.04 0.05 Located in Transylvania (1 yes, 0 no) 0.09** 0.12* 0.04 Located in Banat or Crisana-Maramures (1 yes, 0 no) 0.14* 0.16* 0.004 Judet development index 0.003* 0.001 0.007* R2 0.25* 0.16* 0.18*
*Significantly different from 0 for p=0.01; **Significantly different from 0 for p=0.05
90 Table A2.1 POVERTY INCIDENCE AND PROFILE 1998
Poverty line = 40% of average Poverty line = 60% of average Consumption per adult equivalent consumption per adult equivalent ------Poverty Thou % of Thou Poverty Thou % of Thou Household Headcount poor total non-poor headcount poor total non-poor Characteristics poor poor
TOTAL 11.70 2632.3 100.0 19870.5 33.82 7609.5 100.0 14893.3
Area Urban 8.14 998.7 37.9 11268.1 28.21 3461.0 45.5 8805.8 Rural 15.96 1633.6 62.1 8602.4 40.53 4148.5 54.5 6087.5
Region North - East 17.54 670.8 25.5 3154.0 42.79 1636.8 21.5 2188.0 South East 11.64 407.5 15.5 3092.3 35.74 1250.7 16.4 2249.1 South- West 10.29 249.5 9.5 2174.7 31.41 761.4 10.0 1662.8 West 9.97 203.2 7.7 1834.9 29.97 610.9 8.0 1427.2 North - West 9.73 277.9 10.6 2577.7 30.86 881.1 11.6 1974.5 Center 12.19 322.7 12.3 2324.0 34.22 905.6 11.9 1741.1 Bucharest 5.94 134.5 5.1 2129.7 23.06 522.0 6.9 1742.2
Size 1 person 1.68 25.9 1.0 1516.7 7.01 108.2 1.4 1434.4 2 persons 2.38 94.3 3.6 3861.9 12.87 509.2 6.7 3447.0 3 persons 5.91 291.7 11.1 4642.6 24.00 1184.4 15.6 3749.9 4 persons 9.18 544.1 20.7 5384.8 35.46 2102.4 27.6 3826.5 5 persons 18.64 550.4 20.9 2401.6 51.86 1530.9 20.1 1421.2 >=6 persons 35.31 1125.9 42.8 2062.9 68.19 2174.5 28.6 1014.3
Number of Children No dependent children 6.71 735.1 27.9 10216.6 23.51 2574.6 33.8 8377.1 1 child 11.64 688.3 26.1 5226.4 35.01 2071.0 27.2 3843.8 2 children 14.89 575.1 21.8 3286.8 43.64 1685.2 22.1 2176.7 3 children 29.60 318.8 12.1 758.4 64.58 695.7 9.1 381.5 4 or more children 45.17 314.9 12.0 382.3 83.63 583.1 7.7 114.1
Average adult age Under 30 20.47 844.9 32.1 3281.7 48.20 1989.0 26.1 2137.5 30 – 40 13.77 1316.6 50.0 8241.6 39.60 3785.1 49.7 5773.2 41 - 50 9.16 356.7 13.6 3537.1 31.43 1223.8 16.1 2670.0 51 - 60 4.38 79.6 3.0 1736.2 20.69 375.6 4.9 1440.2 over 60 1.11 34.5 1.3 3073.9 7.59 236.0 3.1 2872.4
Agricultural potential No land 10.24 1218.5 46.3 10685.7 30.76 3661.8 48.1 8242.4 Less than 0.50 ha 18.77 676.7 25.7 2927.5 46.69 1682.8 22.1 1921.4 0.51 ha - 1 ha 15.16 190.3 7.2 1064.6 40.66 510.2 6.7 744.6 1 ha - 2 ha 12.72 311.3 11.8 2136.0 37.38 914.9 12.0 1532.5 2 ha - 3 ha 9.18 149.6 5.7 1479.8 30.43 495.9 6.5 1133.5 er 3 ha 5.17 86.0 3.3 1576.8 20.68 343.9 4.5 1318.9
Occupational status of the household head Employee 8.00 800.9 30.4 9211.9 29.65 2968.4 39.0 7044.4 Employer 0.99 1.7 0.1 173.7 10.12 17.7 0.2 157.7 Free lancer 27.78 223.0 8.5 579.5 53.92 432.6 5.7 369.8 Farmer 26.93 580.2 22.0 1574.0 57.36 1235.6 16.2 918.7 Unemployed 28.23 397.5 15.1 1010.5 59.78 841.7 11.1 566.3 Pensioner 7.09 546.1 20.7 7151.6 25.61 1971.1 25.9 5726.7 Other 32.86 82.8 3.1 169.2 56.46 142.3 1.9 109.8
Education level of the household head Primary(no school incl.) 18.66 818.0 31.1 3565.0 41.66 1826.0 24.0 2557.0 Elementary 15.67 711.3 27.0 3829.5 41.02 1862.7 24.5 2678.1 Skilled 12.09 680.7 25.9 4948.1 38.52 2168.5 28.5 3460.4 High school 7.40 348.5 13.2 4359.0 29.07 1368.3 18.0 3339.1 Post high school 3.44 51.3 1.9 1438.8 18.62 277.5 3.6 1212.7 Higher 1.28 22.5 0.9 1730.1 6.08 106.5 1.4 1646.0
Age of the household head Under 30 10.25 155.4 5.9 1360.2 29.99 454.5 6.0 1061.1 30 - 40 12.64 617.3 23.5 4267.3 37.17 1815.6 23.9 3068.9 41 - 50 16.99 1099.7 41.8 5371.9 43.01 2783.2 36.6 3688.4
91 51 - 60 11.20 457.1 17.4 3625.4 35.79 1461.2 19.2 2621.4 over 60 5.46 302.8 11.5 5245.8 19.74 1095.0 14.4 4453.6
Civil status of the household head Married 11.44 2031.5 77.2 15720.3 34.43 6111.4 80.3 11640.4 Concubine 31.62 151.5 5.8 327.5 61.47 294.4 3.9 184.5 Divorced 12.05 100.0 3.8 730.2 33.37 277.0 3.6 553.1 Widower 10.00 299.8 11.4 2698.6 27.19 815.3 10.7 2183.1 Unmarried 11.18 49.6 1.9 393.9 25.11 111.3 1.5 332.2
Sex of the household head Masculine 11.79 2240.8 85.1 16766.2 34.57 6571.4 86.4 12435.6 Feminine 11.20 391.5 14.9 3104.3 29.70 1038.1 13.6 2457.6
92 Table A2.1 POVERTY INCIDENCE AND PROFILE 1998, IN RURAL AREA
Poverty line = 40% of average Poverty line = 60% of average consumption per adult equivalent consumption per adult equivalent ------Poverty Thou % of Thou Poverty Thou % of Thou Household Headcount poor total non-poor headcount poor total non-poor Characteristics poor poor
TOTAL- RURAL 15.96 1633.6 100.0 8602.4 40.53 4148.5 100.0 6087.5
Region North - East 20.02 432.5 26.5 1727.3 48.24 1041.8 25.1 1117.9 South - East 16.76 212.9 13.0 1057.9 42.27 537.1 12.9 733.6 South 13.32 271.6 16.6 1767.3 39.11 797.4 19.2 1241.4 South - West 11.42 151.7 9.3 1177.2 32.50 431.9 10.4 897.1 West 16.09 123.9 7.6 645.9 39.05 300.6 7.2 469.2 North - West 14.01 190.9 11.7 1172.2 37.37 509.4 12.3 853.7 Center 18.73 196.4 12.0 852.5 39.20 411.2 9.9 637.7 Bucharest 20.95 53.6 3.3 202.3 46.54 119.1 2.9 136.8
Household size 1 person 2.38 18.7 1.1 770.2 9.72 76.7 1.8 712.3 2 persons 3.25 62.4 3.8 1857.8 15.50 297.6 7.2 1622.6 3 persons 10.07 165.2 10.1 1475.5 33.65 552.1 13.3 1088.6 4 persons 12.77 278.0 17.0 1898.6 42.41 923.1 22.3 1253.5 5 persons 21.88 347.6 21.3 1241.1 54.03 858.4 20.7 730.4 >=6 persons 35.91 761.6 46.6 1359.2 67.93 1440.7 34.7 680.1
Number of children No dependent children 8.74 430.0 26.3 4491.6 26.82 1320.0 31.8 3601.5 With 1 dependent child 16.77 373.8 22.9 1855.1 45.11 1005.4 24.2 1223.5 With 2 dependent children 19.54 371.4 22.7 1529.6 49.97 949.9 22.9 951.1 With 3 dependent children 32.44 219.9 13.5 457.9 66.08 447.9 10.8 229.9 With 4 or more dependent children 47.06 238.5 14.6 268.3 83.92 425.3 10.3 81.5
Average adult age Under 30 27.90 513.0 31.4 1325.6 58.54 1076.4 25.9 762.2 Between 30 and 40 21.71 784.0 48.0 2826.9 52.22 1885.7 45.5 1725.2 Between 41 and 50 12.81 253.8 15.5 1728.3 39.24 777.7 18.7 1204.4 Between 51 and 60 5.45 53.2 3.3 922.5 24.25 236.6 5.7 739.1 Over 60 1.62 29.6 1.8 1799.2 9.41 172.1 4.1 1656.6
Agricultural potential With no land 27.52 342.1 20.9 901.2 54.76 680.9 16.4 562.4 Less than 0.50 ha 21.53 585.2 35.8 2132.7 50.62 1375.9 33.2 1342.0 0.51 ha - 1 ha 16.81 182.5 11.2 903.2 43.17 468.7 11.3 617.0 1 ha - 2 ha 13.78 298.4 18.3 1867.1 38.59 835.6 20.1 1329.8 2 ha - 3 ha 9.54 139.8 8.6 1325.9 30.69 449.8 10.8 1015.9 over 3 ha 5.50 85.6 5.2 1472.3 21.67 337.6 8.1 1220.3
Occupational Status of the household head Employee 12.67 361.2 22.1 2488.3 39.20 1117.0 26.9 1732.5 Employer 0.00 0.0 0.0 29.9 11.12 3.3 0.1 26.6 Free lancer 35.98 135.2 8.3 240.6 62.47 234.8 5.7 141.0 Farmer 27.37 564.6 34.6 1497.8 57.83 1192.7 28.8 869.7 Unemployed 31.86 177.5 10.9 379.5 66.01 367.6 8.9 189.3 Pensioner 8.16 347.8 21.3 3913.2 27.37 1166.4 28.1 3094.7 Other 47.17 47.3 2.9 53.0 66.45 66.7 1.6 33.7
Education level of the household head Primary (no education incl.) 18.74 635.3 38.9 2754.9 41.84 1418.5 34.2 1971.7 Elementary 16.31 473.5 29.0 2429.7 40.52 1176.4 28.4 1726.7 Skilled 14.71 309.4 18.9 1794.4 43.66 918.5 22.1 1185.2 High school 13.06 191.5 11.7 1275.4 38.33 562.3 13.6 904.7 Post high school 8.84 18.6 1.1 191.5 27.22 57.2 1.4 152.9 Higher education 3.30 5.3 0.3 156.5 9.60 15.5 0.4 146.3
Age of the household head Under 30 16.28 106.4 6.5 546.9 41.47 270.9 6.5 382.4
93 Between 30 and 40 20.24 358.0 21.9 1410.8 49.84 881.6 21.3 887.1 Between 41 and 50 27.23 630.0 38.6 1684.0 56.94 1317.6 31.8 996.3 Between 51 and 60 14.42 297.0 18.2 1762.1 43.53 896.3 21.6 1162.8 Over 60 7.04 242.2 14.8 3198.7 22.73 782.0 18.9 2658.9
Civil status of the household head Married 15.45 1211.9 74.2 6634.1 41.19 3231.4 77.9 4614.6 Concubine 46.15 113.9 7.0 132.9 73.96 182.6 4.4 64.3 Divorced 24.09 43.7 2.7 137.7 45.75 83.0 2.0 98.4 Widower 12.84 230.3 14.1 1563.7 32.52 583.4 14.1 1210.7 Unmarried 20.10 33.7 2.1 133.9 40.62 68.1 1.6 99.5
Sex of the household head Masculine 16.10 1373.1 84.1 7157.4 41.49 3539.3 85.3 4991.3 Feminine 15.27 260.5 15.9 1445.0 35.72 609.2 14.7 1096.2
94 Table A2.1 POVERTY INCIDENCE AND PROFILE 1997
Poverty line = 40% of average Poverty line = 60% of average consumption per adult equivalent consumption per adult equivalent ------Poverty Thou % of Thou Poverty Thou % of Thou Household headcount poor total non-poor headcount poor total non-poor Characteristics poor poor
TOTAL 9.53 2148.3 100.0 20397.6 30.81 6946.3 100.0 15599.6
Area Urban 6.95 854.4 39.8 11439.3 25.41 3124.4 45.0 9169.2 Rural 12.62 1293.9 60.2 8958.3 37.28 3821.9 55.0 6430.3
Region North - East 16.28 621.2 28.9 3194.9 40.61 1549.9 22.3 2266.2 South - East 9.93 293.1 13.6 2657.4 33.34 983.8 14.2 1966.7 South 7.81 274.1 12.8 3235.9 29.78 1045.4 15.0 2464.6 South - West 7.46 181.3 8.4 2250.6 31.12 756.9 10.9 1675.1 West 7.41 151.9 7.1 1896.9 25.73 527.2 7.6 1521.6 North - West 7.60 217.5 10.1 2645.6 27.57 789.3 11.4 2073.8 Center 11.31 299.8 14.0 2352.1 31.81 843.5 12.1 1808.5 Bucharest 4.81 109.3 5.1 2164.3 19.81 450.4 6.5 1823.2
Household size 1 person 1.73 25.6 1.2 1453.5 5.50 81.4 1.2 1397.7 2 persons 1.93 75.7 3.5 3841.2 10.90 427.0 6.1 3489.9 3 persons 4.28 214.5 10.0 4799.8 20.88 1046.8 15.1 3967.5 4 persons 6.97 403.3 18.8 5386.7 30.55 1768.8 25.5 4021.2 5 persons 14.74 455.2 21.2 2632.5 47.50 1466.6 21.1 1621.2 >=6 persons 29.90 974.0 45.3 2283.9 66.17 2155.8 31.0 1102.1
Number of children With no dependent children 5.29 574.6 26.7 10284.7 21.31 2314.7 33.3 8544.6 With 1 dependent child 8.52 497.8 23.2 5347.4 31.43 1837.4 26.5 4007.8 With 2 dependent children 11.32 445.8 20.8 3493.5 38.09 1500.5 21.6 2438.8 With 3 dependent children 26.27 303.6 14.1 852.1 60.44 698.6 10.1 457.2 With 4 or more dependent children 43.74 326.5 15.2 420.0 79.74 595.2 8.6 151.2
Age of adults under 30 17.59 766.0 35.7 3589.6 43.26 1884.3 27.1 2471.2 between 30 and 40 10.98 1047.9 48.8 8497.5 36.38 3472.9 50.0 6072.5 between 41 and 50 6.61 248.4 11.6 3510.3 28.60 1075.0 15.5 2683.8 between 51 and 60 3.26 60.9 2.8 1809.9 18.62 348.4 5.0 1522.4 over 60 0.83 25.1 1.2 2990.3 5.50 165.7 2.4 2849.7
Agricultural potential With no land 8.83 1069.4 49.8 11041.7 27.92 3381.0 48.7 8730.0 Less than 0.50 ha 17.10 590.3 27.5 2861.8 44.70 1543.2 22.2 1908.9 0.51 ha - 1 ha 12.74 159.5 7.4 1092.5 38.67 484.1 7.0 767.9 1 ha - 2 ha 7.43 180.3 8.4 2245.6 33.07 802.2 11.5 1623.7 2 ha - 3 ha 6.25 104.0 4.8 1559.2 26.43 439.7 6.3 1223.6 over 3 ha 2.73 44.9 2.1 1596.9 18.04 296.2 4.3 1345.5
Occupational status of the household head Employee 6.19 665.3 31.0 10074.8 27.28 2930.3 42.2 7809.9 Employer 2.62 4.3 0.2 160.0 8.68 14.3 0.2 150.1 Free lancer 23.45 168.3 7.8 549.5 48.99 351.6 5.1 366.2 Farmer 23.10 474.0 22.1 1578.0 55.07 1130.0 16.3 921.9 Unemployed 28.92 323.5 15.1 795.0 60.36 675.1 9.7 443.4 Pensioner 5.74 428.8 20.0 7035.7 22.62 1688.5 24.3 5775.9 Other 29.14 84.1 3.9 204.6 54.21 156.5 2.3 132.2
Education level of the household head Primary (no education incl.) 15.78 705.1 32.8 3763.8 38.24 1709.0 24.6 2759.9 Elementary 13.31 634.4 29.5 4132.9 39.21 1869.3 26.9 2898.0 Skilled 9.20 506.8 23.6 4998.9 34.18 1882.1 27.1 3623.7 High school 5.90 267.8 12.5 4275.7 25.37 1152.6 16.6 3391.0 Post high school 1.55 23.4 1.1 1488.5 14.95 226.0 3.3 1285.9 higher 0.61 10.7 0.5 1737.8 6.15 107.5 1.5 1641.1
Age of the household head Under 30 6.68 122.5 5.7 1712.9 25.49 467.9 6.7 1367.5 Between 30 and 40 11.19 560.8 26.1 4449.8 33.45 1676.1 24.1 3334.5
95 Between 41 and 50 13.61 839.3 39.1 5325.6 39.77 2451.8 35.3 3713.1 Between 51 and 60 10.15 422.9 19.7 3742.7 33.66 1402.1 20.2 2763.5 Over 60 3.78 202.8 9.4 5166.7 17.66 948.4 13.7 4421.0
Civil status of the household head Married 9.23 1656.1 77.1 16286.0 31.15 5588.4 80.5 12353.7 Concubine 29.29 148.2 6.9 357.9 59.50 301.2 4.3 205.0 Divorced 8.53 67.8 3.2 727.5 27.94 222.2 3.2 573.1 Widower 8.08 232.3 10.8 2642.2 25.89 744.2 10.7 2130.3 Unmarried 10.25 43.9 2.0 384.0 21.11 90.3 1.3 337.6
Sex of the household head Masculine 9.57 1833.0 85.3 17316.5 31.40 6012.6 86.6 13136.9 Feminine 9.28 315.3 14.7 3081.1 27.49 933.7 13.4 2462.7
96 Table A2.1 POVERTY INCIDENCE AND PROFILE 1997, IN RURAL AREA
Poverty line = 40% of average Poverty line = 60% of average consumption per adult equivalent consumption per adult equivalent ------Poverty Thou % of Thou Poverty Thou % of Thou Household headcount poor total non-poor headcount poor total non-poor Characteristics poor poor
TOTAL - RURAL 12.62 1293.9 100.0 8958.3 37.28 3821.9 100.0 6430.3
Region North - East 17.96 386.7 29.9 1765.9 43.60 938.4 24.6 1214.1 South - East 14.07 178.6 13.8 1090.4 41.92 532.0 13.9 737.0 South 9.03 184.6 14.3 1860.9 33.45 684.1 17.9 1361.4 South - West 8.64 115.4 8.9 1220.8 35.23 470.7 12.3 865.5 West 12.13 93.5 7.2 677.0 34.77 267.9 7.0 502.6 North - West 9.82 134.5 10.4 1234.4 32.97 451.3 11.8 917.6 Center 16.09 169.6 13.1 884.2 37.30 393.1 10.3 660.7 Bucharest 12.17 31.1 2.4 224.7 32.96 84.3 2.2 171.5
Household size 1 person 2.21 16.5 1.3 731.4 7.54 56.4 1.5 691.5 2 persons 2.74 52.1 4.0 1848.0 13.05 247.9 6.5 1652.3 3 persons 6.98 118.9 9.2 1585.5 30.05 512.2 13.4 1192.2 4 persons 10.54 224.1 17.3 1902.7 38.13 810.9 21.2 1316.0 5 persons 16.34 269.2 20.8 1378.4 48.71 802.6 21.0 845.0 >=6 persons 28.84 613.0 47.4 1512.4 65.49 1392.0 36.4 733.4
Number of children No dependent children 6.75 333.9 25.8 4616.1 24.21 1198.5 31.4 3751.5 1 dependent child 12.55 279.7 21.6 1949.2 41.40 922.7 24.1 1306.1 2 dependent children 14.18 263.5 20.4 1595.2 45.82 851.6 22.3 1007.2 3 dependent children 27.31 189.8 14.7 505.1 61.41 426.7 11.2 268.2 4 or more dependent children 43.68 227.0 17.5 292.7 81.26 422.3 11.0 97.4
Average adult age Under 30 22.45 436.2 33.7 1506.9 53.54 1040.3 27.2 902.8 Between 30 and 40 17.58 626.1 48.4 2934.6 49.21 1752.4 45.9 1808.4 Between 41 and 50 8.83 171.4 13.2 1770.2 35.31 685.5 17.9 1256.0 Between 51 and 60 4.17 42.7 3.3 982.0 21.58 221.1 5.8 803.6 Over 60 0.98 17.5 1.4 1764.6 6.88 122.6 3.2 1659.5
Agricultural potential No land 23.16 316.7 24.5 1051.0 50.32 688.2 18.0 679.6 Less than 0.50 ha 19.60 512.5 39.6 2102.7 48.87 1278.1 33.4 1337.1 0.51 ha - 1 ha 13.64 149.0 11.5 943.1 40.19 438.9 11.5 653.2 1 ha - 2 ha 8.14 175.5 13.6 1979.5 33.79 728.1 19.1 1426.9 2 ha - 3 ha 6.34 96.4 7.4 1423.0 26.55 403.4 10.6 1116.0 over 3 ha 2.92 43.9 3.4 1459.0 18.98 285.3 7.5 1217.6
Occupational status of the household head Employee 9.47 299.2 23.1 2860.8 36.68 1159.2 30.3 2000.8 employer 0.00 0.0 0.0 33.0 4.32 1.4 0.0 31.6 Free lancer 29.54 100.2 7.7 239.0 55.88 189.5 5.0 149.6 Farmer 23.56 456.2 35.3 1480.3 55.91 1082.7 28.3 853.8 Unemployed 32.04 147.0 11.4 311.8 64.44 295.7 7.7 163.1 Pensioner 6.22 262.6 20.3 3959.5 24.32 1027.0 26.9 3195.1 Other 27.95 28.7 2.2 73.9 64.63 66.3 1.7 36.3
Education level of the household head Primary 15.63 541.9 41.9 2925.4 38.79 1344.9 35.2 2122.5 Elementary 13.50 408.7 31.6 2617.9 39.71 1202.0 31.5 1824.6 Skilled 10.44 210.3 16.3 1805.0 36.74 740.4 19.4 1274.9 High school 9.42 129.0 10.0 1239.6 34.36 470.2 12.3 898.4 Post high school 1.55 3.2 0.3 206.0 20.97 43.9 1.1 165.3 Higher 0.48 0.8 0.1 164.5 12.49 20.6 0.5 144.7
Age of the household head Under 30 10.60 77.9 6.0 656.4 38.21 280.5 7.3 453.7 30 - 40 17.67 316.3 24.4 1473.7 45.96 822.7 21.5 967.3 41 - 50 20.44 454.1 35.1 1767.7 52.70 1170.9 30.6 1050.8 51 - 60 13.66 294.6 22.8 1862.3 39.88 860.2 22.5 1296.6 over 60 4.51 151.1 11.7 3198.2 20.53 687.6 18.0 2661.8
97 Civil status of the household head Married 12.54 999.8 77.3 6971.0 38.13 3039.1 79.5 4931.7 Concubine 38.04 95.6 7.4 155.7 69.51 174.6 4.6 76.6 Divorced 14.82 28.0 2.2 161.2 39.66 75.1 2.0 114.2 Widower 8.14 136.3 10.5 1537.8 28.24 472.8 12.4 1201.3 Unmarried 20.52 34.2 2.6 132.6 36.14 60.3 1.6 106.5
Sex of the household head Masculine 13.01 1124.3 86.9 7516.3 38.28 3307.4 86.5 5333.2 Feminine 10.52 169.6 13.1 1442.0 31.92 514.5 13.5 1097.1
98 Table A2.1 POVERTY INCIDENCE AND PROFILE 1996
Poverty line = 40% of average Poverty line = 60% of average consumption per adult equivalent consumption per adult equivalent ------Poverty Thou % of Thou Poverty Thou % of Thou Household rate poor total non-poor rate poor total non-poor Characteristics poor poor
TOTAL 5.07 1145.9 100.0 21461.7 19.85 4487.5 100.0 18120.1
Area Urban 3.62 445.3 38.9 11864.2 15.27 1879.6 41.9 10429.9 Rural 6.80 700.6 61.1 9597.5 25.32 2607.9 58.1 7690.2
Region North - East 8.82 336.7 29.4 3479.6 28.40 1083.9 24.2 2732.4 South East 5.06 149.7 13.1 2806.4 19.58 578.8 12.9 2377.3 South 4.69 165.1 14.4 3358.6 20.49 722.0 16.1 2801.7 South West 3.98 97.1 8.5 2343.5 21.15 516.1 11.5 1924.5 West 3.35 68.8 6.0 1985.1 15.12 310.6 6.9 1743.3 North - West 4.70 135.1 11.8 2738.5 18.23 523.7 11.7 2349.9 Center 5.38 143.0 12.5 2515.4 20.02 532.3 11.9 2126.1 Bucharest 2.21 50.4 4.4 2234.5 9.63 220.0 4.9 2064.9
Household size 1 person 1.07 15.8 1.4 1464.4 3.52 52.1 1.2 1428.2 2 persons 0.82 31.8 2.8 3862.9 4.84 188.7 4.2 3706.1 3 persons 1.93 95.3 8.3 4849.4 11.29 558.3 12.4 4386.4 4 persons 2.95 172.1 15.0 5654.0 17.84 1039.6 23.2 4786.6 5 persons 6.51 203.2 17.7 2917.0 30.71 958.1 21.4 2162.0 >=6 persons 18.78 627.6 54.8 2714.0 50.60 1690.8 37.7 1650.9
Number of children No dependent children 2.77 295.7 25.8 10372.9 12.54 1338.4 29.8 9330.3 1 child 4.22 249.6 21.8 5667.7 18.84 1114.6 24.8 4802.7 2 children 5.15 206.7 18.0 3803.7 23.38 937.7 20.9 3072.6 3 children 14.32 169.7 14.8 1015.6 46.08 546.2 12.2 639.1 4 or more children 27.15 224.2 19.6 601.8 66.66 550.6 12.3 275.4
Average adult age Under 30 5.47 990.3 86.4 17107.8 20.40 3691.4 82.3 14406.6 30 - 40 5.39 128.5 11.2 2255.6 23.99 571.9 12.7 1812.2 41 - 50 2.24 20.8 1.8 908.1 17.86 165.9 3.7 763.0 51 - 60 0.80 3.7 0.3 463.4 8.45 39.5 0.9 427.7 over 60 0.35 2.6 0.2 726.8 2.56 18.7 0.4 710.6
Agricultural potential No land 4.54 553.9 48.3 11658.9 16.93 2067.4 46.1 10145.3 Less than 0.50 ha 9.64 314.8 27.5 2950.3 29.69 969.4 21.6 2295.7 0.51 ha - 1 ha 5.15 68.9 6.0 1268.8 25.53 341.6 7.6 996.1 1 ha - 2 ha 3.86 96.2 8.4 2398.9 23.00 573.8 12.8 1921.3 2 ha - 3 ha 4.37 70.7 6.2 1549.4 20.73 335.8 7.5 1284.3 over 3 ha 2.47 41.4 3.6 1635.4 11.89 199.4 4.4 1477.4
Occupational status of the household head Employee 3.25 359.9 31.4 10730.1 16.33 1810.5 40.3 9279.6 Employer 0.00 0.0 0.0 183.0 2.75 5.0 0.1 178.0 Free lancer 12.30 82.5 7.2 588.2 35.10 235.4 5.2 435.3 Farmer 13.69 279.4 24.4 1761.4 41.03 837.4 18.7 1203.4 Unemployed 17.15 159.7 13.9 771.2 46.57 433.5 9.7 497.4 Pensioner 2.90 213.7 18.6 7154.7 13.96 1028.4 22.9 6339.9 Other 15.69 50.8 4.4 273.0 42.40 137.3 3.1 186.6
Education level of the household head Primary 10.05 472.3 41.2 4229.1 28.90 1358.7 30.3 3342.6 Elementary 5.92 297.6 26.0 4729.2 24.57 1235.1 27.5 3791.7 Skilled 4.48 237.9 20.8 5070.9 22.00 1168.1 26.0 4140.7 High school 2.58 112.8 9.8 4260.0 13.28 580.6 12.9 3792.2 Post high school 1.34 19.3 1.7 1421.9 6.94 100.0 2.2 1341.3 Higher 0.34 6.1 0.5 1750.7 2.56 45.0 1.0 1711.7
Age of the household head Under 30 3.61 66.0 5.8 1760.3 15.42 281.6 6.3 1544.6 30 – 40 5.44 278.3 24.3 4833.6 21.71 1110.0 24.7 4001.9
99 41 - 50 7.61 458.4 40.0 5563.5 25.92 1560.7 34.8 4461.2 51 - 60 5.70 244.2 21.3 4043.2 22.52 965.6 21.5 3321.8 over 60 1.85 99.0 8.6 5261.1 10.63 569.6 12.7 4790.5
Civil status of the household head Married 4.92 889.4 77.6 17177.2 19.98 3609.9 80.4 14456.8 Concubine 16.15 80.7 7.0 419.1 41.88 209.3 4.7 290.5 Divorced 5.88 47.0 4.1 752.6 18.99 151.8 3.4 647.8 Widower 3.85 108.5 9.5 2710.7 16.26 458.4 10.2 2360.8 Unmarried 4.79 20.2 1.8 402.1 13.75 58.1 1.3 364.2
Sex of the household head Masculine 5.13 988.9 86.3 18294.4 20.22 3899.4 86.9 15384.0 Feminine 4.72 157.0 13.7 3167.2 17.69 588.1 13.1 2736.2
100 Table A2.1 POVERTY INCIDENCE AND PROFILE 1996, IN RURAL AREA
Poverty line = 40% of average Poverty line = 60% of average consumption per adult equivalent consumption per adult equivalent ------Poverty Thou % of Thou Poverty Thou % of Thou Household headcount poor total non-poor headcount poor total non-poor Characteristics poor poor
TOTAL - RURAL 6.80 700.6 100.0 9597.5 25.32 2607.9 100.0 7690.2
Area North - East 10.85 233.8 33.4 1921.8 33.36 719.1 27.6 1436.6 South - East 6.74 85.9 12.3 1188.1 25.88 329.7 12.6 944.3 South 5.56 114.5 16.3 1943.8 22.61 465.5 17.8 1592.8 South - West 4.85 65.2 9.3 1281.0 24.44 329.0 12.6 1017.3 West 4.75 36.7 5.2 737.0 20.37 157.6 6.0 616.2 North - West 6.75 93.1 13.3 1285.0 22.76 313.6 12.0 1064.5 Center 6.55 69.1 9.9 986.8 24.93 263.3 10.1 792.7 Bucharest 0.87 2.2 0.3 253.8 11.81 30.2 1.2 225.8
Size 1 person 1.24 9.1 1.3 724.5 4.62 33.9 1.3 699.7 2 persons 1.26 23.8 3.4 1868.2 6.21 117.5 4.5 1774.5 3 persons 3.28 56.6 8.1 1669.9 16.40 283.2 10.9 1443.3 4 persons 4.11 87.2 12.4 2032.8 23.69 502.3 19.3 1617.8 5 persons 7.15 117.5 16.8 1526.2 34.17 561.6 21.5 1082.1 >=6 persons 18.62 406.4 58.0 1775.8 50.84 1109.4 42.5 1072.9
Number of dependent children No dependent children 3.15 155.7 22.2 4791.0 15.39 761.2 29.2 4185.5 1 child 7.09 160.8 23.0 2108.5 27.40 621.8 23.8 1647.5 2 children 6.18 114.0 16.3 1730.1 27.69 510.7 19.6 1333.5 3 children 13.73 95.8 13.7 602.0 46.59 325.1 12.5 372.7 4 or more children 32.26 174.3 24.9 365.9 72.03 389.1 14.9 151.1
Age of adults Under 30 7.23 593.8 84.7 7623.2 25.42 2089.2 80.1 6127.9 30 - 40 8.89 83.0 11.8 850.3 37.03 345.6 13.3 587.7 41 - 50 3.89 18.6 2.7 460.7 26.33 126.2 4.8 353.1 51 - 60 1.25 3.1 0.4 244.4 12.34 30.5 1.2 217.0 over 60 0.51 2.2 0.3 418.8 3.90 16.4 0.6 404.6
Agricultural potential No land 12.47 168.7 24.1 1184.2 32.15 435.0 16.7 918.0 Less than 0.50 ha 11.07 278.2 39.7 2236.1 32.53 817.9 31.4 1696.4 0.51 ha - 1 ha 5.77 68.9 9.8 1125.8 27.47 328.2 12.6 866.5 1 ha - 2 ha 3.84 85.2 12.2 2136.0 23.64 525.0 20.1 1696.2 2 ha - 3 ha 4.08 60.3 8.6 1415.7 21.19 312.7 12.0 1163.3 over 3 ha 2.55 39.3 5.6 1499.7 12.29 189.1 7.3 1349.8
Occupational status of the household head Employee 4.99 166.8 23.8 3176.8 23.10 772.3 29.6 2571.3 Employer 0.00 0.0 0.0 32.2 7.60 2.4 0.1 29.7 Free lancer 14.92 47.0 6.7 268.0 39.80 125.3 4.8 189.6 Farmer 13.84 264.1 37.7 1644.5 41.22 786.7 30.2 1121.9 Unemployed 17.87 63.3 9.0 291.2 53.90 191.1 7.3 163.5 Pensioner 3.13 132.8 19.0 4108.5 15.93 675.7 25.9 3565.6 Other 25.79 26.5 3.8 76.4 52.81 54.3 2.1 48.5
Education level of the household head Primary 9.56 343.6 49.0 3251.7 28.68 1031.0 39.5 2564.3 Elementary 6.40 200.6 28.6 2932.4 26.18 820.3 31.5 2312.7 Skilled 4.90 91.8 13.1 1780.7 24.72 462.9 17.7 1409.6 High school 4.62 60.0 8.6 1237.9 20.35 264.2 10.1 1033.7 Post high school 0.00 0.0 0.0 235.5 6.96 16.4 0.6 219.1 Higher 2.88 4.7 0.7 159.3 8.06 13.2 0.5 150.8
Age of the household head Under 30 6.17 44.0 6.3 670.0 22.77 162.6 6.2 551.5 30 - 40 9.34 164.0 23.4 1592.7 32.48 570.6 21.9 1186.1 41 - 50 12.10 265.2 37.9 1926.9 36.56 801.4 30.7 1390.7 51 - 60 6.78 152.1 21.7 2090.3 27.85 624.5 23.9 1617.9 over 60 2.22 75.3 10.7 3317.6 13.23 448.8 17.2 2944.0
101 Civil status of the household head Married 6.66 537.4 76.7 7528.0 25.69 2072.1 79.5 5993.3 Concubine 22.07 56.1 8.0 198.2 51.38 130.7 5.0 123.7 Divorced 13.35 23.8 3.4 154.7 31.56 56.3 2.2 122.2 Widower 4.36 72.0 10.3 1581.4 19.04 314.8 12.1 1338.7 Unmarried 7.68 11.2 1.6 135.1 23.21 34.0 1.3 112.4
Sex of the household head Masculine 6.96 606.5 86.6 8103.3 25.96 2261.1 86.7 6448.7 Feminine 5.93 94.2 13.4 1494.2 21.84 346.8 13.3 1241.5
102 Table A2.1 POVERTY INCIDENCE AND PROFILE 1995
Poverty line = 40% of average Poverty line = 60% of average consumption per adult equivalent consumption per adult equivalent ------Poverty Thou % of Thou Poverty Thou % of Thou Household headcount poor total non-poor headcount poor total non-poor Characteristics poor poor
TOTAL 7.96 1804.4 100.0 20851.7 25.27 5726.0 100.0 16930.2
Area Urban 5.06 623.0 34.5 11700.8 19.01 2342.5 40.9 9981.3 Rural 11.43 1181.4 65.5 9150.9 32.75 3383.5 59.1 6948.8
Region North - East 11.79 450.4 25.0 3369.2 35.21 1344.8 23.5 2474.8 South - East 7.96 235.8 13.1 2724.9 25.10 743.2 13.0 2217.5 South 8.84 312.4 17.3 3220.8 27.47 970.6 17.0 2562.7 South- West 7.45 182.3 10.1 2263.8 25.84 632.1 11.0 1814.0 West 4.10 84.4 4.7 1975.3 17.28 356.0 6.2 1703.7 North - West 7.56 217.7 12.1 2662.5 24.14 695.3 12.1 2185.0 Center 9.60 255.7 14.2 2407.5 25.73 685.4 12.0 1977.8 Bucharest 2.87 65.7 3.6 2227.7 13.02 298.7 5.2 1994.8
Size 1 person 1.09 15.9 0.9 1439.5 5.12 74.6 1.3 1380.9 2 persons 1.49 58.4 3.2 3874.3 8.10 318.5 5.6 3614.2 3 persons 3.38 161.5 9.0 4614.6 15.16 724.1 12.6 4052.1 4 persons 5.33 309.2 17.1 5491.2 22.64 1313.0 22.9 4487.5 5 persons 11.71 362.9 20.1 2737.6 39.05 1210.6 21.1 1889.9 >=6 persons 24.96 896.4 49.7 2694.5 58.07 2085.2 36.4 1505.7
Number of children No dependent children 4.42 470.1 26.1 10161.7 16.35 1737.8 30.3 8894.1 1 child 6.72 385.8 21.4 5357.2 24.60 1412.9 24.7 4330.1 2 children 8.98 363.2 20.1 3681.3 30.12 1218.0 21.3 2826.5 3 children 18.48 234.7 13.0 1035.2 52.78 670.3 11.7 599.6 4 or more children 36.26 350.6 19.4 616.3 71.05 687.0 12.0 279.9
Age of adults under 30 14.79 648.0 35.9 3733.7 38.11 1670.1 29.2 2711.6 30 - 40 8.43 802.9 44.5 8724.4 28.17 2684.0 46.9 6843.3 41 - 50 6.58 257.7 14.3 3657.5 23.65 926.1 16.2 2989.1 51 - 60 3.33 63.4 3.5 1837.6 14.26 271.0 4.7 1629.9 over 60 1.11 32.5 1.8 2898.5 5.96 174.8 3.1 2756.2
Agricultural potential No land 6.29 773.3 42.9 11513.9 21.19 2603.2 45.5 9683.9 Less than 0.50 ha 14.46 453.4 25.1 2683.2 36.53 1145.7 20.0 1991.0 0.51 ha - 1 ha 8.22 112.7 6.2 1259.2 30.45 417.7 7.3 954.2 1 ha - 2 ha 9.91 256.2 14.2 2328.1 31.82 822.4 14.4 1761.9 2 ha - 3 ha 8.09 131.7 7.3 1496.1 27.60 449.3 7.8 1178.5 over 3 ha 4.68 77.1 4.3 1571.2 17.45 287.6 5.0 1360.7
Occupational status of the household head Employee 5.01 562.9 31.2 10661.6 21.28 2389.0 41.7 8835.4 Employer 1.51 2.0 0.1 132.7 4.01 5.4 0.1 129.4 Free lancer 18.36 114.6 6.4 509.6 41.12 256.7 4.5 367.6 Farmer 22.09 441.6 24.5 1557.9 51.88 1037.3 18.1 962.2 Unemployed 21.98 253.8 14.1 901.0 54.15 625.4 10.9 529.4 Pensioner 5.18 373.6 20.7 6839.0 17.80 1283.8 22.4 5928.8 Other 18.28 55.9 3.1 249.9 41.98 128.4 2.2 177.4
Education level of the household head Primary 14.89 751.0 41.6 4294.2 35.30 1781.1 31.1 3264.1 Elementary 10.20 582.6 32.3 5128.7 32.36 1848.3 32.3 3863.0 Skilled 6.84 331.8 18.4 4519.0 27.11 1315.2 23.0 3535.7 High school 2.71 102.9 5.7 3699.5 14.65 557.2 9.7 3245.3 Post high school 1.82 27.2 1.5 1461.1 11.21 166.8 2.9 1321.5 Higher 0.51 9.0 0.5 1749.1 3.27 57.5 1.0 1700.5
Age of the household head Under 30 4.36 75.5 4.2 1656.8 18.79 325.6 5.7 1406.8 30 - 40 8.90 478.9 26.5 4900.5 27.61 1485.4 25.9 3893.9
103 41 - 50 10.74 621.1 34.4 5162.0 32.35 1871.1 32.7 3912.1 51 - 60 8.97 402.0 22.3 4081.7 27.22 1220.5 21.3 3263.2 over 60 4.30 226.9 12.6 5050.7 15.60 823.4 14.4 4454.2
Civil status of the household head Married 7.67 1392.4 77.2 16762.8 25.47 4624.6 80.8 13530.6 Concubine 19.70 106.8 5.9 435.5 44.57 241.7 4.2 300.6 Divorced 9.70 85.6 4.7 796.8 24.68 217.8 3.8 664.6 Widower 7.59 202.7 11.2 2467.1 22.22 593.2 10.4 2076.7 Unmarried 4.17 16.9 0.9 389.4 11.97 48.6 0.8 357.7
Sex of the household head Masculine 7.94 1542.3 85.5 17889.3 25.59 4973.5 86.9 14458.2 Feminine 8.13 262.1 14.5 2962.4 23.34 752.5 13.1 2472.0
104 Table A2.1 POVERTY INCIDENCE AND PROFILE 1995, IN RURAL AREA
Poverty line = 40% of average Poverty line = 60% of average consumption per adult equivalent consumption per adult equivalent ------Poverty Thou % of Thou Poverty Thou % of Thou Household headcount poor total non-poor headcount poor total non-poor Characteristics poor poor
TOTAL - RURAL 11.43 1181.4 100.0 9150.9 32.75 3383.5 100.0 6948.8
Region North - East 14.77 319.0 27.0 1840.2 41.30 891.7 26.4 1267.5 South - East 11.42 145.9 12.4 1131.5 32.29 412.4 12.2 865.0 South 10.23 211.3 17.9 1854.2 30.16 623.0 18.4 1442.5 South West 10.66 144.6 12.2 1211.6 32.72 443.7 13.1 912.4 West 6.08 47.2 4.0 729.2 23.79 184.7 5.5 591.7 North - West 10.61 146.8 12.4 1236.5 29.07 402.1 11.9 981.1 Center 12.76 135.0 11.4 922.8 33.83 357.9 10.6 700.0 Bucharest 12.31 31.6 2.7 224.9 26.44 67.8 2.0 188.7
Size 1 person 1.38 9.7 0.8 694.1 7.59 53.4 1.6 650.4 2 persons 2.28 43.6 3.7 1867.1 11.11 212.4 6.3 1698.4 3 persons 6.29 104.7 8.9 1560.9 22.97 382.6 11.3 1283.0 4 persons 8.05 165.1 14.0 1887.3 29.48 605.0 17.9 1447.4 5 persons 13.26 210.3 17.8 1376.4 41.83 663.8 19.6 922.9 >=6 persons 26.85 647.9 54.8 1765.2 60.77 1466.3 43.3 946.7
Number of children No dependent children 6.09 300.9 25.5 4640.7 20.64 1020.0 30.1 3921.6 1 child 10.20 225.3 19.1 1983.5 34.13 753.9 22.3 1454.9 2 children 14.16 257.6 21.8 1561.6 39.53 719.1 21.3 1100.2 3 children 20.32 146.1 12.4 572.8 57.86 415.9 12.3 302.9 4 or more children 39.06 251.5 21.3 392.3 73.72 474.6 14.0 169.2
Age of adults under 30 21.65 402.4 34.1 1456.8 50.67 942.1 27.8 917.1 30 - 40 13.99 499.1 42.2 3069.7 40.37 1440.8 42.6 2128.0 41 - 50 9.50 199.8 16.9 1902.9 31.21 656.2 19.4 1446.5 51 - 60 4.76 50.1 4.2 1003.2 19.10 201.2 5.9 852.1 over 60 1.71 29.9 2.5 1718.4 8.19 143.1 4.2 1605.2
Agricultural potential No land 15.78 220.9 18.7 1178.6 37.83 529.4 15.6 870.1 Less than 0.50 ha 16.61 402.0 34.0 2018.3 41.22 997.6 29.5 1422.7 0.51 ha - 1 ha 9.01 107.4 9.1 1084.2 31.64 377.0 11.1 814.5 1 ha - 2 ha 10.75 248.3 21.0 2062.4 33.44 772.7 22.8 1537.9 2 ha - 3 ha 8.52 125.8 10.6 1350.8 28.93 427.1 12.6 1049.4 over 3 ha 5.03 77.1 6.5 1456.7 18.23 279.6 8.3 1254.1
Occupational status of the household head Employee 8.30 289.0 24.5 3193.7 30.77 1071.7 31.7 2411.0 Employer 8.30 2.0 0.2 22.6 8.30 2.0 0.1 22.6 Free lancer 24.67 63.1 5.3 192.6 45.09 115.3 3.4 140.4 Farmer 21.89 402.6 34.1 1436.9 52.46 965.0 28.5 874.5 Unemployed 24.92 123.3 10.4 371.5 60.30 298.3 8.8 196.4 Pensioner 6.52 269.9 22.8 3869.6 21.33 883.1 26.1 3256.4 Other 32.98 31.5 2.7 64.1 50.25 48.0 1.4 47.6
Education level of the household head Primary 15.04 574.3 48.6 3244.5 36.19 1382.1 40.8 2436.7 Elementary 11.72 404.0 34.2 3042.1 34.54 1190.3 35.2 2255.7 Skilled 9.47 151.6 12.8 1448.9 33.85 541.8 16.0 1058.7 High school 4.51 48.8 4.1 1034.5 20.96 227.0 6.7 856.3 Post high school 1.23 2.7 0.2 218.1 14.60 32.3 1.0 188.6 Higher 0.00 0.0 0.0 162.7 6.12 10.0 0.3 152.7
Age of the household head Under 30 7.01 47.7 4.0 632.5 27.49 187.0 5.5 493.2 30 – 40 15.61 269.4 22.8 1456.2 41.70 719.7 21.3 1005.9 41 - 50 17.61 391.5 33.1 1831.9 44.43 987.9 29.2 1235.4 51 - 60 12.35 292.1 24.7 2073.4 35.74 845.5 25.0 1520.1 over 60 5.41 180.7 15.3 3156.9 19.28 643.4 19.0 2694.2
105 Civil status of the household head Married 11.15 905.1 76.6 7213.2 33.32 2705.3 80.0 5413.1 Concubine 28.27 74.5 6.3 189.0 54.12 142.6 4.2 120.9 Divorced 19.37 44.1 3.7 183.6 39.14 89.1 2.6 138.6 Widower 9.27 147.1 12.5 1440.2 26.30 417.4 12.3 1169.8 Unmarried 7.78 10.5 0.9 124.9 21.43 29.0 0.9 106.4
Sex of the household head Masculine 11.52 1017.3 86.1 7810.8 33.50 2957.3 87.4 5870.7 Feminine 10.91 164.1 13.9 1340.1 28.33 426.2 12.6 1078.1
106 Table A2.2. Poverty Statistics, 1998 (calculation based on a poverty line at 60% of the average consumption per adult equivalent, Romanian adult equivalent scheme) Household characteristics poverty Average Relative Relative Average GINI Indicator SEN headcoun income gap average income gap poverty Indicator t persons lei income gap FGT1 % severity % % FGT2 % Total 33.82 99294 27.01 9.13 3.56 0.140 0.126 Sex of the household head Masculine 34.57 98614 26.82 9.27 3.59 0.138 0.128 Feminine 29.70 103597 28.18 8.37 3.41 0.149 0.116 Region North-East 42.79 110951 30.18 12.91 5.36 0.152 0.174 South-East 35.29 100500 27.33 9.65 3.76 0.139 0.132 South 35.74 93024 25.30 9.04 3.25 0.125 0.124 South-West 31.41 95283 25.91 8.14 3.15 0.139 0.114 West 29.97 97619 26.55 7.96 3.10 0.139 0.110 North-West 30.86 94796 25.78 7.96 3.09 0.139 0.111 Center 34.22 102531 27.89 9.54 3.87 0.146 0.132 Bucharest 23.06 85146 23.16 5.34 1.84 0.119 0.074 Occupational Status of the household head Employee 29.65 86127 23.42 6.94 2.38 0.118 0.096 Employer 10.12 55196 15.01 1.52 0.40 0.079 0.022 Free lancer 53.92 136476 37.12 20.01 9.83 0.192 0.265 Farmer 57.36 118105 32.12 18.42 7.85 0.156 0.245 Unemployed 59.78 119619 32.53 19.45 8.50 0.162 0.260 Pensioner 25.61 87655 23.84 6.10 2.15 0.123 0.085 Other 56.46 144066 39.18 22.12 11.38 0.203 0.291 Age of the household head
Under 30 29.99 101848 27.70 8.31 3.37 0.147 0.115 Between 30 and 40 37.17 98814 26.87 9.99 3.87 0.139 0.138 Between 41 and 50 43.01 107464 29.23 12.57 5.17 0.149 0.171 Between 51 and 60 35.79 93962 25.55 9.15 3.38 0.130 0.126 Over 60 19.74 85380 23.22 4.58 1.59 0.121 0.064 Education level of the household head
Primary 41.66 116153 31.59 13.16 5.77 0.165 0.179 Elementary 41.02 105975 28.82 11.82 4.71 0.143 0.160 Skilled 38.52 93218 25.35 9.77 3.56 0.127 0.134 High school 29.07 85119 23.15 6.73 2.35 0.120 0.094 Post high school 18.62 68643 18.67 3.48 1.10 0.106 0.051 Higher 6.08 79083 21.51 1.31 0.42 0.106 0.018 Number of children No dependent children 23.51 89241 24.27 5.71 2.07 0.128 0.080 With 1 35.01 96498 26.24 9.19 3.52 0.138 0.127 dependent child With 2 43.64 99283 27.00 11.78 4.56 0.139 0.162 dependent children With 3 64.58 119614 32.53 21.01 9.08 0.158 0.279 dependent children With 4 or more dependent children 83.63 129400 35.19 29.43 13.18 0.163 0.383
107 Civil status of the household head.
Married 34.43 97266 26.45 9.11 3.48 0.136 0.126 Concubine 61.47 131021 35.63 21.91 10.33 0.180 0.290 Divorced 33.37 101609 27.63 9.22 3.69 0.143 0.127 Widower 27.19 100463 27.32 7.43 2.95 0.144 0.103 Unmarried 25.11 112417 30.57 7.68 3.22 0.156 0.104 Agricultural potential
No land 30.76 97301 26.46 8.14 3.20 0.141 0.113 0.00-0.5 ha 46.69 110185 29.97 13.99 5.77 0.150 0.189 0.51-0.99 ha 40.66 102517 27.88 11.34 4.34 0.136 0.153 1.00-1.99 ha 37.38 96230 26.17 9.78 3.60 0.129 0.133 2.00- 3.00 ha 30.43 91529 24.89 7.58 2.72 0.126 0.104 over 3 ha 20.68 81792 22.25 4.60 1.51 0.112 0.064 Household size 1 person 7.01 81513 22.17 1.55 0.58 0.130 0.023 2 persons 12.87 73024 19.86 2.56 0.81 0.106 0.036 3 persons 24.00 81979 22.30 5.35 1.84 0.118 0.076 4 persons 35.46 84882 23.09 8.19 2.79 0.117 0.114 5 persons 51.86 103541 28.16 14.60 5.72 0.140 0.198 >=6 persons 68.19 126705 34.46 23.50 10.51 0.165 0.309 Average adult age
Under 30 48.20 115123 31.31 15.09 6.46 0.158 0.203 Between 30 and 40 39.60 98953 26.91 10.66 4.12 0.138 0.146 Between 41 and 50 31.43 88521 24.08 7.57 2.66 0.121 0.105 Between 51 and 60 20.69 75434 20.52 4.24 1.36 0.107 0.060 Over 60 7.59 65209 17.74 1.35 0.41 0.098 0.020
108 Table A2.2. Poverty Indicators in 1998 – Rural Romania (calculation based on a poverty line at 60% of the average consumption per adult equivalent, Romanian adult equivalent scheme) Household poverty Average Relative Relative Average GINI SEN characteristics headcount income gap average income poverty Indicator Indicator persons % lei income gap FGT1 severity gap % % FGT2 % Total Rural 40.53 107094 29.13 11.80 4.78 0.133 0.156 Sex of the household head
Masculine 41.49 106561 28.98 12.02 4.83 0.131 0.159 Feminine 35.72 110192 29.97 10.71 4.57 0.146 0.143 Region North-East 48.24 111635 30.36 14.65 5.97 0.130 0.190 South-East 42.27 107337 29.19 12.34 5.05 0.135 0.164 South 39.11 97910 26.63 10.41 3.81 0.117 0.138 South - West 32.50 99869 27.16 8.83 3.54 0.135 0.120 West 39.05 108142 29.41 11.48 4.73 0.137 0.153 North West 37.37 104991 28.55 10.67 4.35 0.137 0.143 Center 39.20 121439 33.03 12.95 5.83 0.154 0.170 Bucharest 46.54 110799 30.13 14.03 5.83 0.134 0.184 Occupational Status of the household head Employee 39.20 97563 26.53 10.40 3.82 0.116 0.137 Employer 11.12 74865 20.36 2.26 0.47 0.009 0.023 Free lancer 62.47 147252 40.05 25.02 12.82 0.188 0.321 Farmer 57.83 118675 32.28 18.67 7.99 0.139 0.241 Unemployed 66.01 119790 32.58 21.50 9.49 0.154 0.284 Pensioner 27.37 89587 24.37 6.67 2.33 0.108 0.089 Other 66.45 156034 42.44 28.20 15.45 0.202 0.359 Age of the household head
Under 30 41.47 112548 30.61 12.69 5.48 0.154 0.171 Between 30 and 40 49.84 108047 29.39 14.65 5.88 0.130 0.192 Between 41 and 50 56.94 122188 33.23 18.92 8.35 0.148 0.246 Between 51 and 60 43.53 97788 26.60 11.58 4.32 0.118 0.153 Over 60 22.73 89365 24.30 5.52 1.97 0.111 0.074 Education level of the household head
Primary 41.84 115985 31.54 13.20 5.77 0.149 0.175 Elementary 40.52 108115 29.40 11.92 4.80 0.133 0.157 Skilled 43.66 97856 26.61 11.62 4.32 0.117 0.154 High school 38.33 99578 27.08 10.38 3.92 0.116 0.136 Post high school 27.22 92649 25.20 6.86 2.37 0.107 0.090 Higher 9.60 89405 24.32 2.33 0.84 0.101 0.031 Number of children
No dependent children 26.82 96035 26.12 7.01 2.67 0.126 0.095 With 1 dependent child 45.11 103955 28.27 12.75 5.03 0.128 0.169 With 2 dependent 49.97 105734 28.76 14.37 5.76 0.134 0.192 children With 3 dependent 66.08 124224 33.79 22.33 9.89 0.146 0.287 children With 4 or more dependent 83.92 133843 36.40 30.55 13.76 0.145 0.383 children Civil status of the household head
109 Married 41.19 104811 28.51 11.74 4.65 0.129 0.155 Concubine 73.96 143554 39.04 28.88 14.13 0.169 0.365 Divorced 45.75 125318 34.08 15.59 7.05 0.156 0.203 Widower 32.52 104242 28.35 9.22 3.77 0.136 0.124 Unmarried 40.62 119912 32.61 13.25 5.86 0.151 0.174 Agricultural potential
No land 54.76 126159 34.31 18.79 8.67 0.159 0.245 0.00-0.5 ha 50.62 113883 30.97 15.68 6.61 0.142 0.206 0.51-0.99 ha 43.17 105773 28.77 12.42 4.81 0.124 0.162 1.00-1.99 ha 38.59 98539 26.80 10.34 3.85 0.115 0.136 2.00- 3.00 ha 30.69 93267 25.37 7.78 2.83 0.116 0.104 over 3 ha 21.67 82411 22.41 4.86 1.60 0.096 0.065 Household size
1 person 9.72 84376 22.95 2.23 0.85 0.137 0.033 2 persons 15.50 75348 20.49 3.18 1.02 0.100 0.044 3 persons 33.65 92393 25.13 8.46 3.06 0.112 0.113 4 persons 42.41 92061 25.04 10.62 3.80 0.113 0.142 5 persons 54.03 109952 29.90 16.16 6.56 0.133 0.212 >=6 persons 67.93 128425 34.93 23.73 10.66 0.150 0.303 Average adult age
Under 30 58.54 124780 33.94 19.87 8.99 0.159 0.260 Between 30 and 40 52.22 109240 29.71 15.52 6.25 0.128 0.202 Between 41 and 50 39.24 94130 25.60 10.04 3.61 0.110 0.133 Between 51 and 60 24.25 79607 21.65 5.25 1.71 0.096 0.071 Over 60 9.41 69338 18.86 1.78 0.57 0.106 0.026
110 Table A2.3. Rural Poverty and Living Conditions Poverty Headcount Distribution of Poor poor non poor total poor non poor total Poverty headcount 37.3 62.7 100 100.0 100.0 100 Ownership status Owned 37.0 63.0 100 95.3 96.3 96.0 Rented from state 49.8 50.2 100 2.3 1.4 1.8 Rented from private 31.2 68.8 100 0.2 0.3 0.3 For free 39.1 60.9 100 2.1 2.0 2.0 Dwelling type Apartment 41.5 58.5 100 4.2 3.5 3.8 House 37.1 62.9 100 95.8 96.5 96.2 Construction concrete 38.2 61.8 100 2.1 2.0 2.0 material bricks 30.1 69.9 100 35.1 48.5 43.5 wood 41.4 58.6 100 13.5 11.4 12.2 trellis 43.5 56.5 100 49.3 38.1 42.3 Lighting Electric 36.8 63.2 100 97.1 99.1 98.3 Oil 67.5 32.5 100 2.4 0.7 1.3 Other 60.3 39.7 100 0.5 0.2 0.3 Heating Central heating system 40.5 59.5 100 1.1 0.9 1.0 Own heating system 17.5 82.5 100 0.2 0.6 0.4 natural gas stowe 26.8 73.2 100 4.9 7.9 6.8 oil/coal/wood stowe 38.1 61.9 100 93.8 90.6 91.8 Cooking fuel electricity 27.6 72.4 100 0.6 0.9 0.8 natural gas 26.2 73.8 100 4.4 7.3 6.2 oil/coal/wood 45.5 54.5 100 55.4 39.5 45.4 butelie 30.5 69.5 100 38.1 51.7 46.6 other 57.7 42.3 100 1.5 0.6 1.0 Water supply indoor, public system 30.2 69.8 100 6.8 9.3 8.4 indoor, own system 21.5 78.5 100 2.8 6.1 4.9 outdoor, in courtyard 35.0 65.0 100 3.8 4.2 4.0 outside courtyard 38.7 61.3 100 85.0 79.9 81.8 from river 58.2 41.8 100 0.2 0.1 0.1 other 69.4 30.6 100 1.4 0.4 0.8 Hot Water Supply from public network 42.8 57.2 100 1.2 1.0 1.1 System own system 20.1 79.9 100 5.0 11.7 9.2 do not have 39.0 61.0 100 93.8 87.3 89.7 Sewage System from public network 38.9 61.1 100 3.8 3.6 3.7 own system 22.1 77.9 100 6.0 12.6 10.2 do not have 39.0 61.0 100 90.2 83.8 86.2 Bathroom location indoor 24.4 75.6 100 7.4 13.6 11.3 outdoor 30.1 69.9 100 0.9 1.3 1.1 do not have 39.0 61.0 100 91.7 85.1 87.6 Toilet Location indoor 28.8 71.2 100 5.6 8.2 7.2 outdoor 39.1 60.9 100 45.5 42.2 43.5 do not have 36.9 63.1 100 48.9 49.6 49.3
111 Annex 3 Methodology Used in the Measurement of Poverty
112 Measuring Household Welfare. The “Household Consumption Expenditure” indicator built with data provided by the Integrated Household Survey (IHS) was used as a measure of living standards. Why this one? Several money and non-money indicators may be used to measure the standard of living. Income, consumption or wealth are among the most frequently used money indicators. Of the non-money indicators, household health status, housing conditions and ownership of durables are common. However, money indicators as a measure of poverty somehow restricts the scope of our investigation since they do not include all household physical resources. Living conditions, ownership of durables or cash and in-kind savings are included only to the extent they show in household income or consumption. So, for instance, about 95 per cent of the households own their dwellings, which spares them the cost of rent. On average, this kind of expenditure accounts for a low 0.2 per cent of the monthly household budget. And yet, even though the share of tenant households is low overall, such households are at a disadvantage, with those having a lease agreement with a private landlord being more so.
Fixtures and facilities may also distinguish among living standards and help identify the more vulnerable population segments. Access to central heating, public water supply and sewage system, along with bathrooms and lavatories inside the house which are almost standard in urban area appears more like a luxury in rural area. Except for electricity to which access is fairly general (99% of all households) across the country, all other modern conveniences seem to be hardly accessible to rural households. Structural disparities between the two areas are obvious and bear the mark of the earlier housebuilding investment effort which was largely industrial area-oriented. Rural household welfare can do little to change this fact of life which essentially depends on community resources and local skills to initiate cost-effective investment programmes. Financially, access to modern conveniences in urban area shows in monthly housing maintenance expenditure which is frequently six times as high as in rural area.
While empirical practice has proved the various living standard measures to be highly consistent, it sanctioned money indicators as the most accurate. The experience of household surveys has shown that in most cases income is under-reported, which suggests that household expenditure is far more relevant for the study of poverty. HIS provides detailed information on the current use of foods, non-foods and services, as it includes both cash expenditure and the estimated consumption of household-produced goods.
For the consumption expenditure of households of different sizes to be comparable, we divided it by household adult equivalents and reported it in constant prices over time and by area. The equivalence scale was built with the help of standard food requirements advised by Romanian nutritionists whose recommended caloric intake varies with age and gender.
To compare the rural-urban poverty rate and its year-on-year variations we inflated the 1995 relative poverty line for 40% and 60% of the average consumption expenditure per adult equivalent using the NCS equivalence scale. All the members of the households under this line were regarded as poor.
113 Since earlier papers1 reported Table A3.1 The Adult Equivalence Scale consistently lower food prices in rural Caloric Equivalence area that would require deflator intake coefficients corrections by area, we tested this assumption. We determined the average Boys aged 16 - 20 3600 1.00 household food consumption in (unit Men aged 21 - 65 3500 0.97 value) prices by area. For 1996, the Boys aged 13 – 15 3100 0.86 monthly food basket was three to six per Women aged 21 – 56 2900 0.81 cent lower in rural than in urban area in terms of money – a difference which we Girls aged 13 – 20 2800 0.78 did not regard as significant. First, Children aged 10 – 12 2500 0.69 because we used unit values not prices to Children aged 7 - 9 2100 0.58 estimate the purchase power differential Men aged 66+ 2100 0.58 by area. Given the direct proportionality Women aged 57+ 2100 0.58 between price and quality, the lower unit Children aged 4 - 6 1700 0.47 value in rural area may be linked to a Infants aged 2 - 3 1300 0.36 somewhat poorer quality. Second, there are goods that are seldom bought in rural Infants aged 0 - 1 1000 0.28 area where own consumption is quite frequent. Unfortunately, accurately determined unit values for such goods are not available.
Population estimates have been produced using post-stratification weights. Three were the main steps taken in building post-stratification weights: a) determining the base weights; b) adjusting for non-responses; c) correcting the distribution of the sample population against county demographic characteristics. a. Base weights
The inverse probability for a household to be included in the sample was used to derive the household base weight. All persons in a household have the same base weight (Pb) as the household itself . By multiplying every person by the base weight a first variant of estimates is derived. b. Ajusting for non-responses
The annual non-response rate is put at some 10% of all households in the sample.
The aim of this step is to adjust the previously derived weights by a coefficient computed from non-responses by area and county as shown below:
Re spondenthouseholdweightednumbers + Non -respondentweightednumbers C = a Selectedhouseholdweightednumbers
At the end of this stage, the weight of each household (hence of each household member) shall be the product between the base weight (Pb) and the adjustment coefficient for non-responses (C a).
1 The World Bank, Poverty and Social Policy Report, 1997
114 c. Correction
The distribution of the sample population by age, gender and area differs from the distribution of total population.
In the correction stage, the demographic data published by July 1 are used for the annual HIS sample population. The correction coefficients are determined by a post-stratification technique which divides the sample in 82 strata by area and county.
This yields consistent estimates for per capita variables: for every county the weight derived in the previous stages for each area/age/gender group (cell) is multiplied by a correction coefficient (Cr) computed as shown below:
Totalcountypopulation/area / agebracket/gender/ C = r Weightedsamplepopulationbycounty/area / agebracket/gender/
This is an iterative procedure involving up to 15 steps. By the end of the stage, the extension coefficient for each person in the sample is a product of the coefficients determined in the earlier stages:
Cext = pb × Ca × Cr
115 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 1 ALBA CERU-BACAINTI 431 23.27 1 ALBA POIANA VADULUI 1418 23.21 1 ALBA INTREGALDE 998 22.96 1 ALBA RAMET 879 22.78 1 ALBA PONOR 776 21.64 1 ALBA GARDA DE SUS 1976 21.20 1 ALBA HOREA 2272 20.94 1 ALBA MOGOS 1378 20.39 1 ALBA SCARISOARA 1990 19.53 1 ALBA ARIESENI 1939 19.17 3 ARGES POPESTI 4070 19.56 3 ARGES NUCSOARA 1815 19.51 4 BACAU LIPOVA 2478 22.89 4 BACAU STANISESTI 4523 22.54 4 BACAU FILIPENI 2350 22.48 4 BACAU COLONESTI 1935 21.54 4 BACAU PLOPANA 3064 21.41 4 BACAU HORGESTI 4323 21.19 4 BACAU CORBASCA 5135 21.01 4 BACAU MOTOSENI 3716 20.98 4 BACAU BERZUNTI 5378 20.77 4 BACAU ONCESTI 1697 20.66 4 BACAU SAUCESTI 3446 20.58 4 BACAU GAICEANA 2875 20.57 4 BACAU HURUIESTI 2816 20.56 4 BACAU GHIMES-FAGET 5141 20.55 4 BACAU RACHITOASA 5058 20.51 4 BACAU NEGRI 2936 20.29 4 BACAU TATARASTI 2644 20.10 4 BACAU ROSIORI 1946 19.98 4 BACAU SECUIENI 3824 19.76 4 BACAU IZVORU BERHECIULUI 1568 19.74 4 BACAU VULTURENI 2028 19.61 4 BACAU UNGURENI 3194 19.49 4 BACAU PARINCEA 4038 19.42 4 BACAU BALCANI 8657 19.31 4 BACAU DEALU MORII 3166 19.27 4 BACAU PANCESTI 4078 19.22 4 BACAU GLAVANESTI 3930 19.19 5 BIHOR VARCIOROG 2536 20.22 5 BIHOR SINTEU 1497 20.12 5 BIHOR ROSIA 2812 19.70 6 BISTRITA-NASAUD TARLISUA 4314 26.91 6 BISTRITA-NASAUD ZAGRA 4037 23.10 6 BISTRITA-NASAUD SPERMEZEU 2906 22.55 6 BISTRITA-NASAUD URMENIS 2248 22.27 6 BISTRITA-NASAUD PARVA 2826 21.69 6 BISTRITA-NASAUD MICESTII DE CAMPIE 1418 21.48 6 BISTRITA-NASAUD ROMULI 1824 21.17 6 BISTRITA-NASAUD COSBUC 3407 20.98 6 BISTRITA-NASAUD BUDESTI 2209 20.93 6 BISTRITA-NASAUD REBRA 2893 20.48 7 BOTOSANI PRAJENI 3153 28.37 7 BOTOSANI UNTENI 2918 27.58 7 BOTOSANI LUNCA 4949 26.77 7 BOTOSANI BALUSENI 5065 26.49 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 7 BOTOSANI VORNICENI 4564 26.42 7 BOTOSANI DURNESTI 3852 26.38 7 BOTOSANI DOBARCENI 2568 26.01 7 BOTOSANI HAVARNA 5025 25.95 7 BOTOSANI HANESTI 2297 25.45 7 BOTOSANI VIISOARA 2199 25.31 7 BOTOSANI GORBANESTI 3461 25.21 7 BOTOSANI SANTA MARE 2807 25.14 7 BOTOSANI CORDARENI 2181 25.07 7 BOTOSANI GEORGE ENESCU 3588 25.01 7 BOTOSANI CALARASI 3787 24.97 7 BOTOSANI COPALAU 7219 24.93 7 BOTOSANI MILEANCA 3288 24.92 7 BOTOSANI SUHARAU 5136 24.87 7 BOTOSANI CORLATENI 3971 24.70 7 BOTOSANI TODIRENI 3228 24.62 7 BOTOSANI PALTINIS 3277 24.58 7 BOTOSANI RADAUTI-PRUT 4251 24.45 7 BOTOSANI MIHALASENI 2488 24.45 7 BOTOSANI VACULESTI 2085 24.32 7 BOTOSANI MITOC 1842 24.31 7 BOTOSANI HLIPICENI 3380 24.24 7 BOTOSANI NICSENI 2640 24.15 7 BOTOSANI CRISTINESTI 3804 23.95 7 BOTOSANI POMARLA 3033 23.89 7 BOTOSANI CRISTESTI 4524 23.79 7 BOTOSANI ALBESTI 6151 23.65 7 BOTOSANI UNGURENI 6529 23.64 7 BOTOSANI COTUSCA 5181 23.57 7 BOTOSANI AVRAMENI 5044 23.56 7 BOTOSANI CORNI 6278 23.47 7 BOTOSANI CONCESTI 2053 23.44 7 BOTOSANI HUDESTI 6397 23.27 7 BOTOSANI MANOLEASA 3523 22.84 7 BOTOSANI IBANESTI 3999 22.76 7 BOTOSANI HILISEU-HORIA 3443 22.51 7 BOTOSANI TUDORA 5031 22.51 7 BOTOSANI SULITA 5365 22.24 7 BOTOSANI ROMA 2918 22.17 7 BOTOSANI FRUMUSICA 5808 22.13 7 BOTOSANI STIUBIENI 2745 22.13 7 BOTOSANI VORONA 7811 22.09 7 BOTOSANI RAUSENI 2665 21.91 7 BOTOSANI DRAGUSENI 2716 21.37 7 BOTOSANI SENDRICENI 3799 20.75 7 BOTOSANI ROMANESTI 2247 20.62 7 BOTOSANI STAUCENI 2947 20.41 7 BOTOSANI VLASINESTI 3407 19.84 7 BOTOSANI VLADENI 4605 19.78 7 BOTOSANI DANGENI 2954 19.21 7 BOTOSANI BROSCAUTI 3171 19.17 9 BRAILA VICTORIA 3956 21.50 9 BRAILA CIOCILE 2952 21.38 9 BRAILA ZAVOAIA 3397 21.03 9 BRAILA FRECATEI 1495 20.53 9 BRAILA BERTESTII DE JOS 3204 20.22 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 9 BRAILA SALCIA TUDOR 2992 19.55 9 BRAILA VISANI 2697 19.24 9 BRAILA MARASU 3334 19.20 10 BUZAU CHILIILE 827 26.70 10 BUZAU BISOCA 2962 26.37 10 BUZAU ODAILE 1098 24.86 10 BUZAU VALEA SALCIEI 1008 24.65 10 BUZAU LARGU 1689 24.13 10 BUZAU PARDOSI 559 23.55 10 BUZAU GLODEANU SARAT 4444 23.31 10 BUZAU MARGARITESTI 923 23.17 10 BUZAU BOZIORU 1266 23.06 10 BUZAU GLODEANU-SILISTEA 4407 22.95 10 BUZAU BRADEANU 2740 22.64 10 BUZAU COZIENI 2744 22.56 10 BUZAU COLTI 1379 22.54 10 BUZAU RACOVITENI 1468 22.29 10 BUZAU BUDA 3468 22.28 10 BUZAU SARULESTI 1611 22.28 10 BUZAU MIHAILESTI 3688 21.79 10 BUZAU NAENI 1968 21.68 10 BUZAU ROBEASCA 1313 21.29 10 BUZAU MOVILA BANULUI 2722 21.17 10 BUZAU BREAZA 3309 21.06 10 BUZAU BRAESTI 2773 21.05 10 BUZAU SCUTELNICI 2490 20.75 10 BUZAU VALCELELE 1747 20.49 10 BUZAU PADINA 4797 20.48 10 BUZAU LUCIU 3115 20.44 10 BUZAU GHERGHEASA 2585 20.43 10 BUZAU BLAJANI 1370 20.23 10 BUZAU CHIOJDU 3898 20.04 10 BUZAU MURGESTI 1079 19.65 10 BUZAU AMARU 2522 19.51 10 BUZAU SCORTOASA 3368 19.38 51 CALARASI GURBANESTI 1596 25.78 51 CALARASI SOHATU 3538 25.28 51 CALARASI ULMU 1835 23.51 51 CALARASI NANA 2692 22.63 51 CALARASI FRASINET 1870 22.48 51 CALARASI CASCIOARELE 2304 22.34 51 CALARASI VALEA ARGOVEI 2653 21.80 51 CALARASI ALEXANDRU ODOBESCU 2957 21.57 51 CALARASI ILEANA 3648 21.31 51 CALARASI VLAD TEPES 2652 21.28 51 CALARASI LUICA 2308 20.58 51 CALARASI DOROBANTU 3480 20.56 51 CALARASI INDEPENDENTA 3968 20.45 51 CALARASI GRADISTEA 5090 19.93 51 CALARASI BELCIUGATELE 2000 19.33 11 CARAS-SEVERIN CORNEREVA 3656 25.99 11 CARAS-SEVERIN SOPOTU NOU 1596 25.26 11 CARAS-SEVERIN ZORLENTU MARE 1063 22.08 11 CARAS-SEVERIN COPACELE 1489 20.60 11 CARAS-SEVERIN FARLIUG 2080 20.37 11 CARAS-SEVERIN LUNCAVITA 3034 19.93 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 12 CLUJ SANMARTIN 2012 20.87 12 CLUJ SACUIEU 1800 20.77 12 CLUJ MARISEL 1828 20.70 12 CLUJ RISCA 1986 20.47 12 CLUJ MAGURI-RACATAU 2253 20.22 12 CLUJ SIC 3032 19.39 12 CLUJ CATINA 2119 19.32 13 CONSTANTA DOBROMIR 2328 22.43 13 CONSTANTA DUMBRAVENI 572 19.95 15 DIMBOVITA BARBULETU 6974 19.53 16 DOLJ LIPOVU 3311 24.33 16 DOLJ VELA 2222 24.06 16 DOLJ SECU 1364 23.22 16 DOLJ VARVORU DE JOS 3274 23.13 16 DOLJ ORODEL 3087 22.96 16 DOLJ SEACA DE PADURE 1517 22.94 16 DOLJ GOGOSU 1143 22.82 16 DOLJ VERBITA 1583 22.81 16 DOLJ SALCUTA 2626 22.79 16 DOLJ NEGOI 4081 22.16 16 DOLJ BOTOSESTI-PAIA 979 22.08 16 DOLJ DRANIC 3078 21.88 16 DOLJ RADOVAN 3068 21.78 16 DOLJ BISTRET 4636 21.65 16 DOLJ CARPEN 3043 21.62 16 DOLJ SEACA DE CAMP 2209 21.49 16 DOLJ BRABOVA 1926 21.45 16 DOLJ CERNATESTI 2350 21.40 16 DOLJ CIOROIASI 2050 20.87 16 DOLJ GINGIOVA 2928 20.86 16 DOLJ GRECESTI 2008 20.67 16 DOLJ SOPOT 2324 20.64 16 DOLJ OSTROVENI 5874 20.47 16 DOLJ IZVOARE 2043 20.10 16 DOLJ SADOVA 7997 19.82 16 DOLJ GOICEA 4836 19.55 16 DOLJ DESA 4988 19.42 16 DOLJ CIUPERCENII NOI 6280 19.30 16 DOLJ PREDESTI 3751 19.16 17 GALATI BALASESTI 2222 24.11 17 GALATI JORASTI 1908 23.95 17 GALATI VLADESTI 2141 23.93 17 GALATI CORNI 2479 23.21 17 GALATI CERTESTI 2296 22.96 17 GALATI SCANTEIESTI 2536 22.67 17 GALATI DRAGUSENI 4965 22.09 17 GALATI MATCA 11356 22.01 17 GALATI SMULTI 1651 21.91 17 GALATI BALENI 2545 21.90 17 GALATI PRIPONESTI 2256 21.71 17 GALATI VARLEZI 2219 21.44 17 GALATI OANCEA 1409 21.41 17 GALATI CUCA 2547 21.21 17 GALATI VALEA MARULUI 3391 20.32 17 GALATI BANEASA 2179 20.27 17 GALATI BALABANESTI 3647 20.21 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 17 GALATI TEPU 2415 19.78 17 GALATI COROD 7791 19.17 52 GIURGIU GOGOSARI 2245 26.96 52 GIURGIU STOENESTI 2485 26.56 52 GIURGIU RASUCENI 3210 26.20 52 GIURGIU SCHITU 2266 26.00 52 GIURGIU GOSTINARI 2607 24.44 52 GIURGIU LETCA NOUA 3823 22.97 52 GIURGIU IZVOARELE 5033 22.45 52 GIURGIU COLIBASI 3995 22.27 52 GIURGIU TOPORU 2706 22.25 52 GIURGIU GAUJANI 2882 20.81 52 GIURGIU VARASTI 6283 20.51 52 GIURGIU PUTINEIU 3021 20.27 52 GIURGIU PRUNDU 4880 19.57 52 GIURGIU VANATORII MICI 4978 19.17 20 HUNEDOARA BULZESTII DE SUS 488 25.26 20 HUNEDOARA LUNCA CERNII DE JOS 1327 25.21 20 HUNEDOARA BATRANA 241 22.16 20 HUNEDOARA LELESE 563 22.10 20 HUNEDOARA BALSA 1382 21.09 20 HUNEDOARA BLAJENI 1778 20.98 20 HUNEDOARA VORTA 1259 20.61 20 HUNEDOARA CERBAL 781 19.97 21 IALOMITA BARCANESTI 3964 23.90 21 IALOMITA DRAGOESTI 926 23.24 21 IALOMITA VALEA MACRISULUI 2110 23.13 21 IALOMITA AXINTELE 2796 22.91 21 IALOMITA MOVILITA 4479 22.75 21 IALOMITA SCANTEIA 4286 22.48 21 IALOMITA REVIGA 3331 22.28 21 IALOMITA GHEORGHE LAZAR 2470 22.08 21 IALOMITA GRINDU 2383 22.07 21 IALOMITA SINESTI 2378 21.73 21 IALOMITA SALCIOARA 2559 21.38 21 IALOMITA ALBESTI 2661 21.17 21 IALOMITA BRAZII 3440 21.08 21 IALOMITA BALACIU 3323 20.98 21 IALOMITA COCORA 3633 20.96 21 IALOMITA GRIVITA 7009 20.19 21 IALOMITA CIOCARLIA 891 19.99 21 IALOMITA MILOSESTI 3002 19.54 21 IALOMITA CIOCHINA 3532 19.32 22 IASI COSTULENI 4526 25.16 22 IASI CUCUTENI 1301 24.06 22 IASI BRAESTI 3410 23.68 22 IASI GOLAIESTI 3395 23.62 22 IASI COARNELE CAPREI 2906 22.93 22 IASI PROBOTA 3432 22.75 22 IASI PLUGARI 3478 22.74 22 IASI GROPNITA 3323 22.57 22 IASI PRISACANI 3421 22.39 22 IASI ANDRIESENI 4024 22.32 22 IASI COZMESTI 2895 22.21 22 IASI FOCURI 5952 21.78 22 IASI SINESTI 4186 21.67 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 22 IASI HORLESTI 3299 21.66 22 IASI ROMANESTI 1848 21.57 22 IASI GROZESTI 1812 21.53 22 IASI TRIFESTI 4810 21.51 22 IASI LUNGANI 4516 21.34 22 IASI SIPOTE 4995 21.26 22 IASI TIGANASI 3694 20.90 22 IASI CIORTESTI 4289 20.88 22 IASI CEPLENITA 4533 20.86 22 IASI GORBAN 2760 20.86 22 IASI DOLHESTI 2605 20.75 22 IASI IPATELE 1962 20.70 22 IASI VICTORIA 4034 20.66 22 IASI TIBANA 6302 20.54 22 IASI POPESTI 3597 20.53 22 IASI DOBROVAT 2529 20.48 22 IASI MOGOSESTI 4220 20.40 22 IASI VOINESTI 5543 20.38 22 IASI TUTORA 1971 20.32 22 IASI COMARNA 4133 20.22 22 IASI DUMESTI 4213 20.15 22 IASI SCHITU DUCA 4065 19.95 22 IASI MIRONEASA 4180 19.70 22 IASI OTELENI 3697 19.59 22 IASI MOVILENI 2928 19.57 22 IASI STRUNGA 4329 19.47 22 IASI BIVOLARI 4231 19.35 23 ILFOV NUCI 3093 21.19 24 MARAMURES BARSANA 6733 23.11 24 MARAMURES STRAMTURA 4482 22.78 24 MARAMURES BUDESTI 3835 21.95 24 MARAMURES BOTIZA 2984 21.70 24 MARAMURES SACEL 4056 21.50 24 MARAMURES IEUD 4486 20.75 24 MARAMURES DESESTI 2724 20.12 24 MARAMURES ROZAVLEA 6048 19.51 25 MEHEDINTI PODENI 1327 22.39 25 MEHEDINTI PUNGHINA 3245 22.35 25 MEHEDINTI POROINA MARE 1546 22.00 25 MEHEDINTI PADINA 2252 21.56 25 MEHEDINTI GODEANU 707 21.45 25 MEHEDINTI BALVANESTI 1231 21.38 25 MEHEDINTI BACLES 2880 21.17 25 MEHEDINTI OPRISOR 3247 21.05 25 MEHEDINTI ISVERNA 2792 20.48 25 MEHEDINTI OBARSIA-CLOSANI 1309 20.18 25 MEHEDINTI GROZESTI 2467 19.64 25 MEHEDINTI JIANA 4971 19.53 25 MEHEDINTI DUMBRAVA 2216 19.48 26 MURES SANPETRU DE CAMPIE 2945 19.82 27 NEAMT VALEA URSULUI 3522 25.72 27 NEAMT DAMUC 3104 24.15 27 NEAMT FAUREI 2136 23.70 27 NEAMT HANGU 4177 23.59 27 NEAMT BOZIENI 2731 22.56 27 NEAMT POIENARI 3130 22.46 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 27 NEAMT ONICENI 3122 22.21 27 NEAMT ICUSESTI 4324 21.90 27 NEAMT CANDESTI 4343 21.64 27 NEAMT STANITA 2180 21.62 27 NEAMT BAHNA 3686 20.68 27 NEAMT DRAGOMIRESTI 2326 20.46 27 NEAMT MARGINENI 4100 19.99 27 NEAMT BIRA 4311 19.81 27 NEAMT TIBUCANI 4251 19.74 27 NEAMT MOLDOVENI 2451 19.51 27 NEAMT TUPILATI 2301 19.42 27 NEAMT PIPIRIG 8371 19.38 27 NEAMT BARGAUANI 4206 19.38 27 NEAMT BICAZU ARDELEAN 4147 19.22 28 OLT VADASTRITA 3875 22.48 28 OLT VALENI 3549 21.83 28 OLT TOPANA 1248 21.49 28 OLT RADOMIRESTI 4458 20.85 28 OLT OBARSIA 3221 20.00 28 OLT SPRANCENATA 3101 19.40 29 PRAHOVA JUGURENI 826 25.79 29 PRAHOVA SALCIA 1382 23.36 29 PRAHOVA LAPOS 1604 20.24 29 PRAHOVA SALCIILE 2438 19.80 29 PRAHOVA BALTA DOAMNEI 2737 19.55 31 SALAJ SAG 3533 22.81 31 SALAJ DRAGU 1266 21.09 31 SALAJ FILDU DE JOS 1631 20.15 31 SALAJ ZALHA 1341 19.47 30 SATU MARE CAMARZANA 2757 22.31 30 SATU MARE BATARCI 4156 20.01 30 SATU MARE CERTEZE 5810 19.53 30 SATU MARE TARSOLT 3182 19.18 32 SIBIU JINA 4359 21.58 33 SUCEAVA MOLDOVA-SULITA 2050 24.30 33 SUCEAVA IZVOARELE SUCEVEI 2356 23.75 33 SUCEAVA CAJVANA 7636 23.02 33 SUCEAVA ULMA 2377 22.37 33 SUCEAVA SLATINA 5130 22.36 33 SUCEAVA GRAMESTI 3116 20.49 33 SUCEAVA VULTURESTI 3684 19.89 33 SUCEAVA MALINI 7255 19.57 33 SUCEAVA ZVORISTEA 5986 19.56 34 TELEORMAN NECSESTI 1727 29.60 34 TELEORMAN RASMIRESTI 1167 27.79 34 TELEORMAN CRANGENI 3476 25.91 34 TELEORMAN SACENI 1746 25.62 34 TELEORMAN DIDESTI 1477 25.45 34 TELEORMAN CIOLANESTI 3999 25.26 34 TELEORMAN CALMATUIU 2791 24.79 34 TELEORMAN PLOPII-SLAVITESTI 5216 23.90 34 TELEORMAN SILISTEA-GUMESTI 2881 23.84 34 TELEORMAN MERENI 3400 23.42 34 TELEORMAN CRANGU 3874 23.03 34 TELEORMAN CONTESTI 3727 23.00 34 TELEORMAN SLOBOZIA MANDRA 4811 22.88 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 34 TELEORMAN MARZANESTI 4529 22.79 34 TELEORMAN TATARASTII DE SUS 3526 22.68 34 TELEORMAN TALPA 2548 22.56 34 TELEORMAN PIETROSANI 3593 22.52 34 TELEORMAN SCURTU MARE 2392 22.42 34 TELEORMAN MAGURA 3286 22.28 34 TELEORMAN ZAMBREASCA 2050 22.18 34 TELEORMAN BRAGADIRU 5074 22.05 34 TELEORMAN CALINESTI 4205 21.87 34 TELEORMAN BUJORU 2192 21.83 34 TELEORMAN VITANESTI 3170 21.82 34 TELEORMAN BUJORENI 1223 21.34 34 TELEORMAN TATARASTII DE JOS 4704 21.10 34 TELEORMAN VIISOARA 2448 20.88 34 TELEORMAN CALMATUIU DE SUS 2912 20.81 34 TELEORMAN DOBROTESTI 5494 20.78 34 TELEORMAN DRACSENEI 3989 20.74 34 TELEORMAN VARTOAPE 3523 20.70 34 TELEORMAN PUTINEIU 2958 20.42 34 TELEORMAN STEJARU 2414 20.41 34 TELEORMAN TROIANUL 3776 20.18 34 TELEORMAN FURCULESTI 4096 20.14 34 TELEORMAN BOGDANA 3130 20.00 34 TELEORMAN STOROBANEASA 3954 19.88 34 TELEORMAN FRUMOASA 2800 19.78 34 TELEORMAN NASTURELU 3128 19.77 34 TELEORMAN BALACI 2566 19.67 34 TELEORMAN SEACA 3135 19.66 34 TELEORMAN TRIVALEA-MOSTENI 3535 19.55 34 TELEORMAN CERVENIA 3625 19.37 34 TELEORMAN SFINTESTI 1648 19.22 35 TIMIS BARA 440 27.78 35 TIMIS SECAS 335 24.54 35 TIMIS OHABA LUNGA 1258 19.73 36 TULCEA HAMCEARCA 1565 20.10 37 VASLUI VOINESTI 3605 28.56 37 VASLUI BOGDANITA 1381 28.26 37 VASLUI BOGDANA 1753 27.89 37 VASLUI IANA 3708 27.84 37 VASLUI DRAGOMIRESTI 4282 27.82 37 VASLUI COROIESTI 2149 27.00 37 VASLUI ALEXANDRU VLAHUTA 2865 26.63 37 VASLUI MALUSTENI 2849 26.60 37 VASLUI GARCENI 2247 26.15 37 VASLUI BLAGESTI 1721 26.12 37 VASLUI GHERGHESTI 2628 26.00 37 VASLUI PUNGESTI 3098 25.95 37 VASLUI DELESTI 5002 25.67 37 VASLUI HOCENI 2981 25.52 37 VASLUI ARSURA 1852 25.43 37 VASLUI BOGDANESTI 3075 25.39 37 VASLUI OSESTI 2931 25.34 37 VASLUI POGANA 3039 25.29 37 VASLUI MICLESTI 2825 25.20 37 VASLUI DANESTI 2417 24.79 37 VASLUI BEREZENI 5181 24.08 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 37 VASLUI PUIESTI 4838 23.84 37 VASLUI SOLESTI 3745 23.81 37 VASLUI TACUTA 3121 23.74 37 VASLUI ZAPODENI 3934 23.73 37 VASLUI OLTENESTI 2875 23.67 37 VASLUI BANCA 5272 23.62 37 VASLUI DIMITRIE CANTEMIR 2831 23.56 37 VASLUI PERIENI 4827 23.38 37 VASLUI EPURENI 3208 23.36 37 VASLUI COSTESTI 3059 23.35 37 VASLUI BACANI 2516 23.19 37 VASLUI VINDEREI 4694 23.16 37 VASLUI BUNESTI-AVERESTI 2903 23.04 37 VASLUI DUMESTI 3766 22.98 37 VASLUI POIENESTI 3044 22.95 37 VASLUI DUDA-EPURENI 4980 22.92 37 VASLUI REBRICEA 3333 22.90 37 VASLUI IVESTI 4436 22.88 37 VASLUI PADURENI 4400 22.76 37 VASLUI VIISOARA 3761 22.59 37 VASLUI VALENI 6587 22.56 37 VASLUI LIPOVAT 4060 22.18 37 VASLUI VETRISOAIA 3571 22.09 37 VASLUI GRIVITA 4741 22.04 37 VASLUI STANILESTI 5961 22.03 37 VASLUI TATARANI 2563 21.64 37 VASLUI LUNCA BANULUI 3734 21.62 37 VASLUI VUTCANI 2232 21.37 37 VASLUI ROSIESTI 3381 21.35 37 VASLUI TODIRESTI 5166 21.18 37 VASLUI ALBESTI 3094 21.04 37 VASLUI CRETESTI 1812 20.80 37 VASLUI TUTOVA 5049 20.57 37 VASLUI GAGESTI 2067 20.53 37 VASLUI BOTESTI 2245 20.26 37 VASLUI LAZA 6209 19.68 37 VASLUI TANACU 5734 19.56 37 VASLUI DRANCENI 4516 19.45 37 VASLUI STEFAN CEL MARE 3042 19.39 38 VILCEA PERISANI 4071 22.74 38 VILCEA MADULARI 2062 21.25 38 VILCEA DANICEI 2636 20.40 38 VILCEA ROSIILE 3316 20.22 38 VILCEA RUNCU 1270 20.04 38 VILCEA BOISOARA 2116 19.74 39 VRANCEA CHIOJDENI 2343 29.64 39 VRANCEA SLOBOZIA BRADULUI 4689 29.36 39 VRANCEA POIANA CRISTEI 2930 29.34 39 VRANCEA VINTILEASCA 2244 28.23 39 VRANCEA REGHIU 2715 27.66 39 VRANCEA NEREJU 4128 27.55 39 VRANCEA ANDREIASU DE JOS 2014 26.31 39 VRANCEA NISTORESTI 2361 26.07 39 VRANCEA VRANCIOAIA 3015 26.02 39 VRANCEA BARSESTI 3685 25.49 39 VRANCEA MERA 3942 25.25 Communes belonging to the poorest quintile, by judets
CODE JUDET JUDET NAME COMUNA NAME POPULATIONPOVERTY INDEX 39 VRANCEA GURA CALITEI 3120 25.17 39 VRANCEA DUMITRESTI 5252 25.16 39 VRANCEA BORDESTI 1898 24.57 39 VRANCEA JITIA 1731 24.04 39 VRANCEA PALTIN 3699 24.01 39 VRANCEA CAMPURI 4016 23.76 39 VRANCEA BALESTI 2107 22.88 39 VRANCEA VIZANTEA-LIVEZI 4297 22.69 39 VRANCEA RACOASA 3574 22.55 39 VRANCEA BOGHESTI 1731 21.76 39 VRANCEA TULNICI 5957 21.63 39 VRANCEA CIORASTI 3902 21.51 39 VRANCEA SIHLEA 5228 21.33 39 VRANCEA SLOBOZIA CIORASTI 2142 20.46 39 VRANCEA NANESTI 2739 20.10 39 VRANCEA CORBITA 2002 20.03 39 VRANCEA TAMBOESTI 4317 19.66 39 VRANCEA TANASOAIA 2285 19.65