Abstract
This paper analyses the Household Budget Surveys prepared by the Turkish Statistical Institute to reveal the empirical importance of precautionary saving in Turkey. The most difficult aspect of the empirical analysis is the approximation of labour income risk as a proxy variable for future labour income uncertainty. Individual disposable income is interacted alternately with the probability of being unemployed and with the probability of job loss in the next period to generate the labour income risk variables. The econometric results support the precautionary saving hypothesis and labour income risk emerges as one of the main determinants of household saving decisions. Moreover, households implement alternative strategies to smooth out their income streams such as holding a second job and increasing the number of income earners in the family. However, it is evident that they are still vulnerable against labour income risk, which underlines the need for an effective and efficient social security system.
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Notes
The introduction of permanent income and the social and demographic characteristics into the econometric investigation process aims to capture life-cycle effects such as saving for the retirement period. However, the precautionary motive for saving is independent of life-cycle motives and it emerges only if there is uncertainty about future income prospects. Therefore, the approximation of the household head’s labour income risk enters the household saving equation as an independent variable.
A settlement unit like a village or town is defined as an urban region, if the total population of the place is equal to or >20,000. If the population is <20,000, it is considered to be a rural region. However, this definition of a rural region does not take into account economic sectors such as the role of the agricultural sector or tourism revenues in the local economy. Therefore, social and economic characteristics of rural regions might differ significantly between the west and east of the country.
Consumption expenditures are available only at household level and as monthly figures in the TURKSTAT Household Budget Surveys. Monthly consumption expenditures are multiplied by twelve to reach an annual estimate of household consumption expenditures with the assumption that household consumption follows a steady pattern throughout the year. Moreover, in the surveys individual disposable income is available both monthly and annually, but household disposable income is available only annually. Thus, household saving is calculated as the difference between annual household disposable income and annualised household consumption expenditures.
Durable goods, which are considered as part of household saving, are home appliances, medical equipment, consumer electronics, new and second-hand automobile purchases and jewellery and watches for personal consumption. The second definition of household saving (SAVII) includes durable goods, but it does not contain housing wealth.
TURKSTAT collects individual and household disposable income figures for the twelve-month period prior to the survey month, but not for the calendar year due to the design of the survey questionnaires. For instance, if a household participates in the Household Budget Survey in September 2008, then the annual household disposable income will refer to the twelve-month period between September 2007 and September 2008. However, monthly inflation rates are quite high and there are significant differences in the inflation rates of geographical regions in Turkey. TURKSTAT has included a regional and monthly inflation variable in the Household Budget Surveys since 2003. Household disposable income and household consumption are inflated to the year-end (December) prices of the corresponding survey year by multiplying with this inflation index. Annual household disposable income and household consumption expenditures are divided by year-end consumer price indices for each survey year and all economic variables including household saving figures are analysed in 2003 TL prices.
The TURKSTAT Household Budget Surveys do not provide information about individuals’ ages. Instead, the surveys specify the age intervals of the individuals. The empirical analysis can only be realised with respect to individuals’ age groups, which increase in 5 year intervals. For that reason, it is not possible to create a pseudo-panel data set using birth cohorts, since it is not possible to determine the individuals’ birth years exactly. Remaining age groups, which are not shown in Table 1, are children between 0 and 5, 6 and 14, 15 and 19 besides individuals, who are 65 or older.
According to Browning and Lusardi (1996), a potential uncertainty measure must be an observable variable, but an exogenous one to the individual’s decisions and behaviour. Finally, a potential uncertainty measure must be variable across the population to account for the heterogeneity in society.
Carroll et al. (2003, p. 586) points out that “… a tenured college professor who, by choice, teaches or consults every other summer may have more variable annual income than a factory worker, but does not face the uncertainty of being laid off during a recession.”
The common approach in the previous literature is to approximate labour income risk by using the individuals’ subjective evaluation of unemployment risk. However, the TURKSTAT Household Budget Surveys do not have a question about the individuals’ subjective evaluation of the probability of being unemployed in the next period, which restricts the scope of the empirical analysis. At the same time, it is observed that the objective probability of being unemployed in the next period is also used to generate labour income risk. For instance, Guariglia and Kim (2004) used the objective probability of unemployment risk to develop a second approximation of labour income risk to check the robustness of their econometric results.
A shortcoming of the TURKSTAT Household Budget Surveys is that no information is provided about the income and employment prospects of the individual, if he/she is already unemployed. Hence, it is possible to observe the employment sector and job status of the individual, only if he/she is currently employed. Moreover, it is not possible to learn whether an individual has social security coverage or not, if he/she is unemployed.
Time dummy variables for survey years are included in the probit model and the omitted year is 2003, since it represents a stable period of the Turkish economy. Only the regression coefficients of the dummy variables for 2008 and 2009 are positive and statistically significant in the probit model, which indicates that household heads’ unemployment risk started to increase in this period compared to 2003 due to the contraction of the economy.
Time dummy variables for survey years are also included in the probit model and the omitted year is 2003 as before, since it is a stable period of the Turkish economy. In this probit model, the regression coefficients of the dummy variables for 2007, 2008 and 2009 are positive and statistically significant, which indicates that household heads’ job loss risk started to increase in this period compared to 2003 due to the contraction of the economy.
The estimation of the permanent component of household heads’ disposable income is discussed in detail in the former working paper version of the paper (Ceritoğlu 2011).
Labour income risk and permanent income are generated variables, which are predicted using auxiliary regressions at the previous stages of the empirical analysis. Therefore, the standard errors of the pooled OLS regressions are estimated using the bootstrap method with 1,000 replications.
I restricted the sample set for household heads, who are 54 and younger and then performed the pooled OLS regressions. The estimated increases in household saving due to labour income risk are higher for the restricted sample compared to the original sample. If the elasticity of uncertainty is calculated at the sample means, it is observed that a 10% rise in the first approximation of labour income risk (LIRI) leads to an increase of 3.8% in the first definition of household saving (SAVI) and an increase of 2.5% in the second definition of household saving (SAVII). In addition to that a 10% rise in the second approximation of labour income risk (LIRII) leads to an increase of 4.2% in the first definition of household saving (SAVI) and an increase of 2.7% in the second definition of household saving (SAVII). Consequently, the precautionary motive for saving is stronger for younger households as expected.
I re-estimated the pooled OLS regressions including interaction terms between the time dummy variables for survey years and the labour income risk variables. The time dummy variables and interaction terms with the first labour income risk (LIRI) are statistically significant in the estimated regression for the first definition of household saving (SAVI). I carried out a joint significance test for the interaction terms. The calculated F-value is 18.80 and the probability value is 0.000. Thus, the null hypothesis that the parameters are jointly equal to zero is rejected. The time dummy variables and interaction terms with the second labour income risk (LIRII) are also statistically significant in the estimated regression for the first definition of household saving (SAVI). I carried out a joint significance test for the interaction terms. The calculated F-value is 19.79 and the probability value is 0.000. Thus, the null hypothesis that the parameters are jointly equal to zero is rejected once again.
I restricted the sample set to households only from urban regions of the country to check the robustness of the econometric results. The econometric results for the restricted sample are very similar to the results presented in the paper. Moreover, I observed that the calculated elasticity of uncertainty and its effect on household saving decisions remains the same, even when the sample set is restricted to households from urban regions of the country. The main difference between urban and rural regions of the country is the situation of unpaid family workers, mostly employed in the agriculture sector. However, these observations have already been removed from the sample set.
Housing investment is not included in the second definition of household saving (SAVII).
It is statistically significant only when the second definition of household saving (SAVII) is regressed on the first definition of the first labour income risk (LIRI) without the interaction terms, which is presented in the fifth column of Table 4.
I introduced the interaction terms for the labour income risk variables alternately as in Guariglia and Kim (2004), but the interaction terms were not statistically significant in the pooled OLS regressions as before.
I also performed joint significance tests for the interaction terms that are included in the pooled OLS regressions. The calculated F-value is 1.26 and the probability value is 0.28 for the OLS regression, which is presented in the third column of Table 4. Thus, the null hypothesis that the parameters are jointly equal to zero cannot be rejected. The calculated F-value is 0.36 and the probability value is 0.70 for the OLS regression, which is presented in the sixth column of Table 4. Thus, the null hypothesis that the parameters are jointly equal to zero cannot be rejected once again. Moreover, the calculated F-value is 0.28 and the probability value is 0.76 for the OLS regression, which is presented in the third column of Table 5. Thus, the null hypothesis that the parameters are jointly equal to zero cannot be rejected. The calculated F-value is 0.15 and the probability value is 0.86 for the OLS regression, which is presented in the sixth column of Table 5. Thus, the null hypothesis that the parameters are jointly equal to zero cannot be rejected once again.
The total number of individuals that benefit from unemployment insurance is only 362, while there are 9,666 unemployed people out of 112,205 individuals in the labour force according to the TURKSTAT Household Budget Surveys.
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Acknowledgments
I would like to thank Richard Cornes, Alessandra Guariglia, Sarah Brown, Sarah Bridges, Murat G. Kırdar and Ercan Uygur for their valuable comments and suggestions.
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Ceritoğlu, E. The impact of labour income risk on household saving decisions in Turkey. Rev Econ Household 11, 109–129 (2013). https://doi.org/10.1007/s11150-011-9137-2
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DOI: https://doi.org/10.1007/s11150-011-9137-2