Private and public transfers: substitute or complement?
expand article infoAnna A. Mironova, Lydia A. Shenshina§
‡ HSE University, Moscow, Russia
§ Ernst & Young, Moscow, Russia
Open Access


The paper analyzes the relationship between private and public social transfers in Russia. The research relies on the data from the Russian Longitudinal Monitoring Survey (RLMS-HSE) carried out by the Higher School of Economics in 1994–2018. The household is the unit of the analysis, the method of logistic regression is applied. The study has shown that when a household receives public social transfers, it is less likely to receive private transfers. So, the findings appear to bear out the hypothesis that public transfers crowd out private transfers in Russia.


households, household incomes, social policy, social transfers, private transfers

JEL codes: D1, D6, D14


Public transfers often serve to compensate for shortcomings of the system of public (social) transfers of funds — in particular, in difficult periods of socio-economic upheavals and/or reforms (Ovcharova and Prokofieva 2000). Reforms aimed at optimizing and improving a system of social security sometimes get in the way of providing citizens in need with a timely and effective assistance.

Countries with a weak social security system normally have a high level of unofficial transfers among relatives, friends, neighbors. Private transfers are the most important element of incomes and expenditures in nearly every developing nation (Rempel and Lobdell 1978; Cox and Jimenez 1990; Maitra and Ray 2003). Within families, meanwhile, most of intergenerational transfers are directed from children to parents. At the same time, in nations with well developed social security systems, private transfers are usually smaller in scale and flow mostly in the opposite direction. For instance, in the U.S. the transfers from younger to older generations are negligible, the main recipient of the transfers are younger generations (Cox and Rank 1992; Niimi and Horioka 2018).

From the modernization perspective, nuclearization of families and the rise of publicly funded social security are parallel and mutually reinforcing processes. The core of this approach is the idea that as a welfare state develops, public transfers crowd out private transfers. This hypothesis has been supported by some studies carried out by economists (Reil-Held 2006). Sociological research, however, demonstrates that public transfers are far from crowding out private transfers. As public transfers influence private ones, the latter, adapting to new circumstances, gain a new form without losing their significance (Kohli 1999; Künemund and Rein 1999; Albertini and Kohli 2012). As a welfare state develops, material exchanges are becoming increasingly more often replaced with barter of various services, including emotional support. So, the development of a public social security system tends to be conducive to, rather than crowd out, solidarity within families.

The goal of this study is to analyze the relationship between private and public (social) transfers in Russia. Can we say that public transfers crowd out private ones?

An analysis of correlations between private and public transfers can help to illuminate certain key questions of social policy planning, for instance: who is the real beneficiary of social payments? Do public transfers indeed crowd out traditional support provided by relatives?

An analysis of the nature of relationship between public and private transfers can provide indirect indications of the efficiency levels of the social support provided for needy groups. In the context of discussions of the need to reform the system of social support, results of such analysis can be used as a foundation for fine-tuning certain support measures.

Private transfers in Russia came into the academic spotlight during the difficult period of socio-economic transformations — that is in the late 1990s – early 2000s, when informal support from relatives and friends was the means of survival for Russian households. The common feature of Russian studies of that period was the approach to transfers as poverty reduction mechanism before all (Rimashevskaya 1997; Ovcharova and Prokofieva 2000; Denisenko 2001). Later Russian researchers studied the social essence of private transfers applying various sociological theories: for instance, network analysis theory (Gradoselskaya 1999) and reciprocity theory (Barsukova 2005). One can also identify what can be arguably called a separate research topic — studies of private transfers in rural areas (Fadeeva 1999; Lylova 2002; Shteinberg 2003).

Recent times have seen the publication of economic-demographic studies of private transfers applying the methodology from the UN’s global project National Transfer Accounts. Thus, Irina Kalabikhina and Zhadra Shaikenova (Kalabikhina and Shaikenova 2018) assessed the distribution of time transfers within households. They showed that the production of goods and services in households was a significant contribution to national economies, accounting, according to different estimates, for 3.9–21% of the annual GDP. The researchers pointed to gender differences in the production of time transfers by members of households. It turned out that during the 24-hour cycle women’s time transfers, on the average, were three hours longer than men’s. In another study, researchers approached private transfers as a component of the national transfer accounts through the lens of the life cycle theory (Denisenko and Kozlov 2018). It was revealed that the specifics of intergenerational transfers were conditioned by the characteristics of individual life cycles. From this point of view, the main function of intergenerational transfers is financing life cycle deficits that arise at certain periods of life when material needs and ability to earn enough to satisfy them are at odds. The study showed that in Russia “a deficit-free period of life” starts at the age of 23 and continues until 56.

At the same time, the relationship between private and public social transfers — the subject that produced many studies outside Russia — has rarely been addressed by researchers in Russia.

Correlations between private and public transfers

Some researchers argue that the streams of private transfers, whose main component is private intergenerational transfers, are conditioned by the nature of the welfare state (Attias-Donfut et al. 2005; Albertini et al. 2007; Björnberg and Latta 2007; Brandt and Deindl 2013). At the same time, researchers don’t argue that there is a simple correlation between private and public transfers — for instance, the more social support a state delivers, the smaller the stream of private transfers. The picture is significantly more complex. State institutions shape norms, obligations and expectations of participants of private intergenerational exchanges. In the Bengtson and Silverstein model (Silverstein and Bengtson 1997), intergenerational relations are a complex and multi-dimensional system that comprises such interconnected aspects as affinity, opportunity structure and function. Providing basic financial assistance and support in the form of services, the state promotes the growth of other elements of intergenerational relationships (Brandt et al. 2009).

Some studies show that the traditional social policies weaken families’ and friends’ responsibility for providing financial assistance because assistance recipients have less need for this sort of support (Emery 2016). For instance, it has been demonstrated that in countries where education is expensive and social guarantees for young people are scarce, the young are more likely to receive financial support from their parents (Villanueva 2005). It was also established that when parents’ pension income grows, private financial transfers from their children decline because state pensions contribute significantly to poverty reduction in seniors’ households (Jensen 2004). A German analysis demonstrates that the bigger public payments to seniors are, the less frequently they receive private transfers, and they act as donors themselves (Reil-Held 2006). Other studies show that in countries that actively support their citizens (for instance, in Scandinavian countries) families tend to provide only limited financial support. And, conversely, in Southern European countries, where the state offers fewer social guarantees, families provide larger amounts of financial assistance (Attias-Donfut et al. 2005; Albertini and Kohli 2012). At the same time nobody seems to claim that family ties in Scandinavian countries are weaker than elsewhere. Since Scandinavian nations have a high level of social support, exchanges within families take a form that is most convenient and desirable for participants of the exchanges (Brandt and Deindl 2013).

In modern research, private transfers are viewed as an element of the system of social interconnections, which also includes public social transfers (Szydlik 2008). Cultural, economic, social, and political institutions can influence private exchanges at the family level, for instance, reducing an individual’s financial needs by providing welfare payments or influencing expectations and social norms.

Thus, for instance, an expansion of public transfers to one group of population can cause increases in private transfers to a completely different group. A study relying on German data found a strong correlation between public transfers to seniors and volumes of private financial assistance to a young generation (Reil-Held 2006). Through private intergenerational transfers, some public transfers received by seniors are directed to younger generations, so private transfers depend on public ones (Kohli 1999). Public transfers enable seniors to provide financial assistance to their children, receiving, in return, non-material help and attention. A research across 13 European countries shows that when a state redistributes public assistance to benefit senior generations (for instance, increasing retirement payments or reducing child support), causing a relative decline in living standards of working-age population in comparison to pensioners, private transfers within families from older to younger generations grow in volume (Mudrazija 2016). In other words, exchanges within families cause a redistribution of funds, redirecting a certain portion of pension payments, financed by working-age population, from seniors to younger generations in a form of private transfers. In this case, private financial assistance from seniors amounts to a backflow of public transfers, which arguably demonstrates a lack of efficiency in the distribution of public resources (Reil-Held 2006).

Although the functions of private and public transfers are similar, there are also noticeable differences between them. One of the differences is an unregulated character of private transfers, in contradistinction to public ones. Public transfers are often predicated on formal assessments of recipients’ need, although informational inadequacies make the redistribution inefficient. Private transfers, meanwhile, are based on a more credible information about recipients’ real needs (Cordes, Goldfarb and Watson 1986).

Research methods

For the purpose of this study, private transfers are considered to be material resources received by a recipient for free from some members of his/her household. Public social transfers include the following payments: pensions, unemployment benefits, and child benefits. If a household has been in receipt of at least one of the above, it means it received social transfers.

This study uses data from the Russian Longitudinal Monitoring Survey — Higher School of Economics (RLMSHSE): a non-governmental project of monitoring socio-economic situation and health of population of the Russian Federation, carried out by the HSE University, ZAO Demoscope, the Institute of Sociology of the Russian Academy of Sciences (Russia), and the Carolina Population Center at the University of North Carolina at Chapel Hill.

The RLMSHSE data have a significant advantage, since in this database, the volume of private transfers received by households is broken down by its source. It also contains information about the receipt of various public transfers by more than 3,000 households in each round of the survey in 1994–2018. This allows us to assess the relationship between private and public transfers in Russia. At the same time, the data has certain limitations. Thus, pensions are not broken down by category. As for child benefits, only the post–2000 data separates payments for children under 1.5 years of age and children aged 1.5–16. Our analysis uses a variable reflecting the receipt of pensions by members of a household, as well as a variable reflecting the receipt of any of the two child benefits mentioned above.

For this study, we selected several years with a five-year spacing interval between them: 1994, 2000, 2005, 2010, 2015, and 2018. In 1999, the survey was not carried out, so we relied on data for the following year, 2000; the last year in the sample is 2018 — the newest RLMSHSE dataset available at the time of the research. The analysis was carried out at the household level using the logistic regression.

The probability of a household to be a recipient of private transfers is a dependent variable. It turns 1 if a household has received financial assistance from relatives and 0 if it hasn’t. The independent variables are as follows:

  1. residence area (urban/rural);
  2. whether the household has debts;
  3. presence of children aged under 7 years in the household;
  4. presence of children aged 7–18 years in the household;
  5. whether per capita income in the household is lower than the regional poverty line (PL);
  6. whether the household has savings;
  7. whether the household receives public social transfers.

All analyzed variables are dichotomous.


The study shows that the share of households receiving assistance from their relatives grew in 1994–2005 from 18.9% to 24.1%, to sharply decline afterwards — down to 20.1% in 2010 (Table 1). There are two plausible explanations for this: a reduced intensity of private exchanges between households in the wake of the 2008 economic crisis and a strengthening of the state’s social assistance. In 2015, this share grew to 22.8%, and then slightly declined in 2018. 70.7% households were receiving public social transfers in 1994, in 2000 the share of receivers went down significantly, to 64.5%, and in 2005, grew back to its 1994 level (70.8%). By 2010, the share went down slightly (to 68.2%), then grew up to 71.5% in 2015, and went down again, to 66.4%, in 2018.

Table 1.

Sample distribution, %

Household characteristics
1994 2000 2005 2010 2015 2018
Yes No Yes No Yes No Yes No Yes No Yes No
Residing in urban area 70.1 29.9 66.9 33.1 67.6 32.4 68.7 31.3 69.2 30.8 69.1 30.9
Debts 19.7 80.3 26.8 73.2 20.2 79.8 26.9 73.1 14.2 85.8 13.8 86.2
Children aged under 7 years 21.2 78.8 13.7 86.3 13.2 86.8 18.2 81.8 16.5 83.5 13.7 86.3
Children aged 7–18 years 34.9 65.1 35.0 65.0 29.1 70.9 25.0 75.0 24.5 75.5 23.5 76.5
Per capita income lower than regional PL 27.9 72.1 67.0 33.0 39.5 60.5 17.6 82.4 16.9 83.1 12.8 87.2
Savings 11.3 88.7 11 89 12.7 87.3 17.2 82.8 15.8 84.2 15 85
Private transfers 18.9 81.1 22.1 77.9 24.1 75.9 20.1 79.9 22.8 77.2 22.2 77.8
Public transfers 70.7 29.3 64.5 35.5 70.8 29.2 68.2 31.8 71.5 28.5 66.4 33.6

More than 50% of respondents in the sample live in a city; this indicator remained more or less constant throughout the entire observation period. The only noticeable fluctuations are from 70.1% in 1994 to 69.1% in 2018, which may have been caused by an increase in the sample size between these years.

The share of households borrowing money was changing during the survey period in line with fluctuations in the economic situation, i.e., the share of households with debt tends to grow in post-crisis years. Thus, the estimates show the share of such households grew from 19.7% in 1994 to 26.9% in 2000 and from 20.2% in 2005 to 26.9% in 2010 — following the crises of 1998 and 2008, respectively. In subsequent years the share declined to 14.2% and 13.8% in 2015 and 2018, respectively.

The share of households with children aged under 7 years was declining during the observed period: from 21.2% in 1994 to 13.7% in 2000. The share fluctuated slightly in the years that followed: it grew from 13.2% in 2005 to 18.2% in 2010, then declined to 16.5% in 2015 and to 13.7% in 2018. The share of households with children aged 7–18 years also declined after 2000: from 35% in 2000 to 23.5% in 2018.

The share of households with savings grew slightly during the observed period: from 11.3% in 1994 to 17.2% in 2010. By 2018, this share declined to 15%.

Our analysis of socio-demographic characteristics reveals significant differences between recipients of private and public social transfers (Table 2). Over the period under review, the share of recipients of public social transfers in rural areas was slightly higher than the share of recipients of private transfers. The subsample of recipients of private transfers has a higher share of borrowers than the subsample of recipients of public social transfers. The share of households with children (in both age groups: aged under 7 and 7–18) is much higher among private transfer recipients than among public transfer recipients during the entire period under review. Prior to 2010, the subsample of public social transfer recipients has a far larger share of poor households (whose income is lower than their regional PLs); however, starting from 2010, a larger share of poor households is observed among recipients of private transfers. With regard to savings, the opposite is true: prior to 2010, savings are more common among private transfer recipients, and then the situation reverses.

Table 2.

Socio-demographic characteristics of households receiving private and public transfers, %

Period 1994 2000 2005 2010 2015 2018
PrT PubT PrT PubT PrT PubT PrT PubT PrT PubT PrT PubT
Residence area
urban 73.8 67.8 70.6 65.4 70.4 65.3 73.0 66.5 72.3 67.6 77.8 68.4
rural 26.2 32.2 29.4 34.6 29.6 34.7 27.0 33.5 27.7 32.4 22.2 31.6
total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
yes 25.5 17.3 33.1 22.4 26.7 17.4 26.7 19.8 7.8 5.1 16.8 11.5
no 74.5 82.7 66.9 77.6 73.3 82.6 73.3 80.2 92.2 94.9 83.2 88.5
total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Children aged under 7 years
yes 34.3 21.0 17.6 11.8 17.0 13.1 26.7 18.7 26.1 14.9 19.9 9.7
no 65.7 79.0 82.4 88.2 83.0 86.9 73.3 81.3 73.9 85.1 80.1 90.3
total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Children aged 7–18 years
yes 41.3 34.3 36.8 28.1 30.3 27.1 28.8 22.9 29.3 20.3 28.3 14.9
no 58.7 65.7 63.2 71.9 69.7 72.9 71.2 77.1 70.7 79.7 71.7 85.1
total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Per capita income lower than regional PL
yes 24.7 27.7 63.3 72.4 39.2 44.1 20.8 17.8 18.7 16.6 14.3 9.4
no 75.3 72.3 36.7 27.6 60.8 55.9 79.2 82.2 81.3 83.4 85.7 90.6
total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
yes 12.8 11.8 14.0 11.2 13.9 12.9 17.4 18.6 14.0 17.7 13.2 17.1
no 87.2 88.2 86.0 88.8 86.1 87.1 82.6 81.4 86.0 82.3 86.8 82.9
total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

The regression analysis shows that not all factors in the model are significant over the entire period under review (Table 3). It should be noted, however, that practically all independent variables, excluding debts, are significant for at least one year in the sample. Such variables as presence of children aged 7–18 in the household, a household’s income below the corresponding regional PL, as well as savings, are significant only in one of the selected periods.

Table 3.

Results of the logistic regression analysis. Factors influencing private transfers

Period 1994 2000 2005 2010 2015 2018
Factor Coeff. St. err. Coeff. St. err. Coeff. St. err. Coeff. St. err. Coeff. St. err. Coeff. St. err.
Urban residence area -0.39 0.05 -1.17*** 0.15 -0.89** 0.1 -0.7** 0.08 -0.81*** 0.09 -0.68*** 0.11
Debts in the household -0.04 0.01 -0.21 0.03 0.01 0.01 0.16 0.02 0.07 0.01 -0.9 0.15
Presence of children aged under 7 years 0.89*** 0.11 0.61* 0.08 0.1 0.01 0.21 0.02 0.69** 0.08 0.42* 0.07
Presence of children aged 7–18 0.23 0.03 0.3 0.04 0.01 0.01 0.09 0.01 0.3 0.03 -0.43** 0.07
Per capita income lower than regional PL -0.07 0.01 -0.59** 0.08 0.17 0.02 0.31 0.03 0.14 0.02 0.27 0.04
Savings in the household -0.01 0.01 -0.15 0.02 -0.15 0.02 -0.15 0.02 -0.22 0.02 -0.38** 0.06
Household received public social transfers -0.86*** 0.11 -1.37*** 0.17 -0.47* 0.05 -0.68*** 0.07 -1.18*** 0.13 -1.44*** 0.23
Constant 2.30*** 0.85 3.81*** 0.83 2.84*** 0.87 2.82*** 0.88 3.14*** 0.86 2.94*** 0.78
R2 0.04 0.10 0.03 0.01 0.07 0.08
N 880 872 872 1498 1286 1361

From the viewpoint of this study’s objective, the most interesting subject is correlations between private and public transfers, controlling for the other variables. Table 3 suggests that receiving public social transfers is significant for every year over the observed period, and this variable reduces the probability of the household to receive private transfers. Other factors negatively affecting the probability of receiving private transfers are living in an urban area (true for all periods excluding 1994), presence of children aged 7–18, and savings (the latter two correlations occur only in the model for 2018). Another noteworthy point is a negative correlation between income below PL and the probability of receiving private transfers: this factor is significant only in 2000. This finding can be partly explained by a post-crisis situation, when households derived a large portion of their incomes from “off the books” employment and, accordingly, did not report it to interviewers; another reason can be close relatives’ inability to provide assistance due to their impoverishment. One other possible explanation is that during crises, or when the income level goes down, or a child is born, households whose members are kin tend to unite. This tendency is quite common among low-income families with children (Pilkauskas and Garfinkel 2016). Presence of children aged under 7 is a factor raising the probability of receiving private transfers.

Our analysis of marginal effects shows that the factors maximally reducing the probability of receiving private transfers, in addition to the constant, are public transfers (years 1994, 2000, 2015, 2018) and living in an urban area (2005 and 2010). The largest marginal effect of public transfers was recorded in 2018, and the largest marginal effect of living in an urban area — in 2000.

To identify variations in the impact of the factors under review on probability of receiving each type of the transfers (public and private), we estimated the model for public transfers (Table 4).

Table 4.

Results of the logistic regression analysis. Factors influencing public transfers

Period 1994 2000 2005 2010 2015 2018
Factor Coeff. St. err. Coeff. St. err. Coeff. St. err. Coeff. St. err. Coeff. St. err. Coeff. St. err.
Urban residence area -0.41* 0.09 -0.16 0.04 -0.42** 0.09 -0.71*** 0.16 -0.52*** 0.11 -0.74*** 0.14
Debts in the household -0.36* 0.08 -0.53** 0.12 -0.54** 0.11 -0.61*** 0.14 0.12 0.02 -0.57* 0.11
Presence of children aged under 7 years 0.38* 0.08 -0.4* 0.09 -0.15 0.03 0.4** 0.09 0.04 0.01 -0.41** 0.08
Presence of children aged 7–18 0.44** 0.1 -0.62*** 0.14 -0.13 0.03 -0.05 0.01 -0.51*** 0.11 -1.03*** 0.2
Per capita income lower than regional PL 0.42** 0.09 0.23 0.05 0.49** 0.1 -0.03 0.01 -0.07 0.01 -0.96*** 0.19
Savings in the household 0.42 0.09 0.11 0.02 0.15 0.03 0.46** 0.1 0.57** 0.12 0.53*** 0.1
Household received private social transfers -0.85*** 0.16 -1.39*** 0.3 -0.46* 0.1 -0.68*** 0.15 -1.19*** 0.25 -1.42*** 0.28
Constant 1.14*** 0.64 1.98*** 0.59 1.47*** 0.67 1.59*** 0.61 2.18*** 0.66 2.74*** 0.62
R2 0.05 0.08 0.03 0.04 0.05 0.14
N 881 872 872 1498 1286 1361

The probability of receiving public social transfers is negatively affected by such factor as living in an urban area (in all years except for 2000). Another negative factor is indebtedness (in all years except for 2015). A possible explanation is that the growing indebtedness could have been occasioned by the absence or insufficiency of public social transfers, which can cause the demand for private transfers to grow. The savings factor, to the contrary, has a positive effect on the probability of receiving social transfers (2010, 2015 and 2018). What can partially explain this correlation is the fact that households receiving public social transfers do not consume goods or services that require a loan, mortgage or otherwise, while also having savings “for a rainy day” (Karavai and Tikhonov 2015). On the other hand, it is possible that households can save precisely because they regularly receive social transfers. Incomes below regional PLs increase the probability of receiving public social transfers in 1994 and 2005 and reduce it in 2018. This can be partially explained by the reforms of the system of public social transfers, the decline in poverty rates, as well as the introduction of social payments to pensioners whose income was below regional PLs. The children factor has a positive correlation with public social transfers in 1994, and negative correlation in 2000 and 2018. A possible explanation of these findings can be that the applied methodology relies on pension payments as the crucial component of public social transfers.

Not unexpectedly, the correlation between private and public transfers is negative. Among all of the model’s factors, with the exception of the constant, receipt of private transfers has the strongest impact on the probability of receiving public transfers. A possible explanation for this is the fact that child and unemployment benefits, as well as certain types of disability pensions, and other social payments can be secured by citizens by declaration of need — they have to submit a set of documents to a corresponding state agency. It is also worth noting that the validity of findings is limited by the types of payments we included in the category of public social transfers.

Comparative analysis of the factors having an impact on the probability of receiving private and public social transfers reveals the following variations: debts negatively correlate with the probability of receiving public social transfers (except in 2015), while playing no big role in relation to the probability of receiving private transfers. The factor of income below regional PL negatively affects the probability of receiving private transfers in 2000, whereas it does not play a significant role in relation to public transfers in 2000, correlates with it it positively in 1994 and 2005 and negatively — in 2018. The savings factor positively correlates with the probability of receiving public transfers (2010, 2015, and 2018) and negatively correlates with the probability of private transfers in 2018; for other periods the factor does not play a significant role. The impact of the presence of children aged under 7 is sinificant in 2000 and 2018: it positively correlates with private transfers and negatively with public social transfers.

Discussion of the findings

Our study shows that when a household receives public social transfers, it is less likely to receive private transfers. So, the findings based on Russia’s data bear out the hypothesis that private financial transfers are crowded out by public transfers. Public transfers, due to this fact, can be regarded as a substitute for private transfers.

It should be noted, though, that this study is focused on material transfers alone, leaving out instrumental transfers (help in the form of various services). The benefits transferred are special because, on the one hand, they substitute for public transfers or commercial goods/services, and on the other, have a unique nature. For instance, such services as childcare or intergenerational loans can be easily substituted for one or another form of public support or commercial services. Transfers within families, however, have a unique character: assistance within a family usually does not depend on specific selection criteria (for instance, correct targeting or need). Besides, exchanges within families often involve unique benefits without apparent commercial substitutes: love, care, emotional attachment (Laferrere and Wolff 2006).

Meanwhile, it is also important to reckon with the fact that the probabilities of receiving private and public transfers are inevitably interrelated to some degree (Emery 2016). For instance, individuals with wealthy relatives (parents, for example) are less likely to become recipients of public social transfers, which, in Russia, are often doled out according with such criteria as targeting and need.

Another important consideration is that the nature of interconnection between private and public social transfers is often shaped by the type of social policy being pursued (Emery 2016). One can identify two main types of social policy: passive and active. One of the key goals of passive social policy, the more traditional and common type, is providing guarantees against various risks: unemployment, poverty, old age, disability, etc. Pursuing this policy, the state guarantees a minimal quality of life and level of income. The concept of welfare state of this kind was formed fairly long ago, when industrial production was the staple of economy. The question of how well the passive system of state support conforms with modern realities is now being actively debated by researchers (ibid.). The active social policy prioritizes investment in social capital (including investment in education and professional retraining, promotion of active ageing, etc.) and tries to prevent possible failings rather than compensate for them (Morel et al. 2012).

Active and passive approaches to social policy shape various social stereotypes with regard to assistance to relatives and friends. When a state pursues a passive social policy, an increase in public financial assistance results in a decline of financial assistance from relatives and friends. In this system private and public transfers function as substitutes. Within the framework of active social policy, meanwhile, when someone receives public social transfers, this sends a signal to this person’s close circle that (s)he needs support. So, public transfers serve as catalysts for private ones, and the two are mutually complementary. At the same time, it is obvious that the distinction between passive and active social policies is very elusive and in practice one can be at difficulty trying to identify a particular social support system as passive or active. And yet, if we give certain consideration to the typology of social policy when we analyze the mutual connections between private and public transfers in our further research, we shall be enabled to better learn the nature of this interconnection.


The findings reveal negative correlation between private and public social transfers in Russia. This warrants approaching private and public transfers as substitutes and arguing that private transfers make up for the “failings” of the public assistance.

Recipients of public and private transfers have different socio-demographic profiles. For the period under review, in rural areas the share of public transfer recipients is slightly larger than the share of private transfer recipients. The share of indebted households is larger among private transfer recipients. The share of households with children is much higher among private transfer recipients than among public transfer recipients.

The comparative analysis of factors having an impact on the probability of receiving private and public transfers reveals the following differentials: savings increase the probability of receiving public transfers (2010, 2015, and 2018), where for private transfers this factor is significant only in 2018, and the correlation is negative. Presence of children aged under 7 and 7–18 in the household has the opposite effect on the probability of receiving private and public transfers. This can be perhaps explained by changes in the streams of private assistance at different periods of a child’s life (Pilkauskas and Garfinkel 2016). Low income levels positively correlate with public transfers in 1994 and 2005 and negatively — in 2018. Besides, a low income negatively correlates with private transfers in 2000.

Limitations of the research

When considering and interpreting the results obtained in this study, we should keep in mind the following limitations.

Data limitations

The RLMSHSE data have certain limitations that complicate the task of interpreting the results. In particular, pensions and child benefits are not broken down by type. Another limitation is due to a large number of missing values for the variables reflecting the receipt of private transfers, which limits our ability to identify more narrow groups of recipients (for instance, with respect to different types of public transfers).

The most important factor in assessing the giving and receiving of private transfers is the presence of relatives living separately from the respondent. The RLMSHSE questionnaires and data, however, do not include questions about the presence and number of such relatives, a circumstance which somewhat obscures the clarity of the findings.

With reference to data prior to 2010, the variable reflecting borrowing was based on the following question from the RLMSHSE questionnaire: “Has you family borrowed money, from official institutions or private parties, during the last 30 days?” Starting from 2015, when the borrowing question was split into two — one for institutional loans and another for loans from private creditors — the borrowing variable absorbs both questions. For the purpose of research consistency, the debt answers recorded in 2015 and later were not separated into different categories, because debts recorded in pre-2015 answers cannot be broken down by type. Addressing these components separately, however, can bring more nuance to the overall picture.

Limitations of the research design

The results obtained in this study should be interpreted taking into account which components were included in the composition of social transfers.


The study was carried out within the Basic Research Program at the National Research University Higher School of Economics (HSE).

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Information about authors

Anna Alekseevna Mironova, Ph.D. (sociology), researcher at the Institute for Social Policy, HSE University, Moscow, Russia. E-mail:

Lydia Anatolievna Shenshina, M.A. (sociology), senior expert at Ernst & Young (CIS) B.V., Moscow branch, Moscow, Russia. E-mail:

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