Do psychological factors emanating from a financial crisis affect consumption? Evidence from China.

Voon, Jan P. ; Ruifang, Zhang

INTRODUCTION

An economic downturn in one source country, wrought by a financial crisis, epidemic, recession or any other exogenous shock, can exert huge negative contagion effects on the domestic economy as well as on other countries. In recent decades, recessionary impacts have become more frequent and the scale of the recessions has also become more complex and larger. (1) Enormous tangible and intangible (or psychological) costs are often incurred, including job losses, job insecurity, pay reductions, wealth evaporation due to losses of personal assets or investments, experience of work stress, and pessimism about the future, among others. (2) In light of the above, this article develops and tests a model of consumption with different psychological states emanating from an economic downturn explicitly built into the model. There has been a paucity of research on the psychological determinants of consumption: previous studies have been mainly devoted to examining the effects of financial crises on unemployment. (3)

Our model is applicable under economic uncertainty when consumption decision processes are subject to various economic and psychological influences. (4) There are many different psychological influences. Anticipatory emotions, for instance, are immediate ex ante visceral reactions often felt before the consumption, whereas anticipated emotions such as regret and disappointment are ex post. (5) This article focuses on anticipatory feeling in which psychological influences serve as antecedents to consumption. More specifically, psychological changes such as anxiety, job insecurity perception and future optimism/pessimism were the outcomes of the global financial crisis (GFC) and these changes may then affect household consumption. Past efforts were directed to research on permanent income (6) or life cycle models of consumption. (7) These models examine how changes in income affect consumption. The behavioural life-cycle model of consumption advanced by Shefrin and Thaler explored how income and wealth (current and future) could affect consumption, but psychological states were not modelled. (8) Caplin and Leahy examined anticipatory feeling under economic uncertainty, but they focused on expected utility, not consumption per se. (9) Moreover, their analysis was theoretically focused.

This article recognises that different groups of consumers (e.g., employed and unemployed) may be affected to a different extent by an uncertain event, such as the GFC. For example, employed workers appear to be subject to higher job insecurity and more employment stress than unemployed people. From our random household survey, about one out of three of the sample population in China was unemployed--which this article defines as those who are not in the labour market, such as retirees, housewives and full-time students. The data set was divided into two categories, namely the employed and unemployed, for a comparative study of the consumption behaviour between them. The psychological states and consumption patterns of these two groups of consumers were affected differently by the GFC.

The next section of this article outlines the model, specifically the role of psychology in consumption. The questionnaires, household survey data and some data description are presented in the third section. The fourth section presents the results arising from the empirical analysis. The conclusions and some policy implications are given in the final section.

ROLE OF PSYCHOLOGICAL VARIABLES

According to the conservation of resources (COR) theory, (10) the loss of economic resources due to a recession can yield immense psychological influence. (11) Psychological heuristics are very important in influencing economic decisions. (12) During a recession, the economic environment can become very uncertain. The cognitive and affective processes could be the dominant pathways affecting consumption decisions because the analytic processes may be impeded by ignorance of the event, inability to control the situation, market failures, or the fact that information about a financial crisis is too complicated.

Mental Accounting

Mental accounting refers to one's ability to subjectively frame transactions in one's mind involving intertemporal consumption or saving decisions. Current and future assets are divided, albeit mentally, subjectively, or psychologically, into separate non-transferable portions. One detailed application of mental accounting, the behavioural life-cycle hypothesis, posits that people mentally frame assets as belonging either to current income, current wealth, or future income and this has implications for their behaviour as the accounts are largely non-fungible and the marginal propensity to consume out of each account is different.

During a financial crisis, the current income and current wealth (two distinct accounts) of individuals may be adversely affected by investment losses, job losses, pay cuts, reductions in values of properties and shares, etc. Besides, current (present) value of income or wealth may also decrease with a declining expected future stream of income, which mentally drive people to save more for the future or, equivalently, consume less now.

Emotion

Different psychological states can emerge from the GFC. Emotions play an important role in decision-making (exemplified by consumption in this article). (13) Emotions are prime candidates for turning a thinker into an actor. (14) The importance of emotions in decision-making was affirmed in the work of Kahneman who distinguished affective heuristic from "deliberative rationality". (15) Rational decision-making may depend on prior emotion processing. (16) Emotions can streamline decision-making. (17) There are different types of emotions. Consistent with the COR theory, anxiety is identified, in this article, to be important during an economic downturn when resource loss is rampant.

Social Development Optimism

In finance, lack of confidence is a source of systemic risk leading to contractions of financial markets. A slew of negative data, information cues and announcements over the media associated with the GFC evoked a lower level of confidence and poorer sentiments. Consumers, especially those whose jobs were unstable, were more pessimistic about their future. Consumers in China may feel even less optimistic about their future in times of a financial crisis due to relatively inadequate social safety nets. Hence, the level of optimism/pessimism emanating from the GFC is seen as an antecedent of consumption. In this article, we investigate whether or not optimism/ pessimism about future social development (or public policy) correlates with household consumption.

Job Insecurity Perception

Decisions can be influenced by two categories of risk: risk as feelings refers to individuals' fast, instinctive and intuitive reactions to danger whereas risk as analysis brings logic, reason, and scientific deliberation to bear on risk assessment. (18) A financial crisis may be viewed as a "hazardous" event. (19) However, unlike many other hazards, an objective, scientific or analytical assessment of a financial crisis is very difficult, if not intractable, because the event is primarily driven by uncertainties (information failure and lack of knowledge). We observe that even experts are confused, shocked or perplexed by the event. Hence, perception, involving intuitive and affective responses to the problem, seems to be a relevant concept.

An important risk perception pertains to workers' job insecurity conditions. Job insecurity has long been regarded as a perception. (20) The unemployment, retrenchment, and bankruptcy rates as well as other information cues arising from the GFC increase employees' subjective perception of job insecurity for employed workers. When jobs are under threat, or when employers exert greater demand on their employees in order to meet their bottom line, workers tend to experience more stress at work. Such a perception, when prolonged, depending on the duration of a financial crisis, may be as detrimental as job loss itself. (21) Hence, we examine if job insecurity perception significantly correlates with household consumption.

THE DATA

The empirical examination in this article utilises the household survey data conducted and pilot-tested by the National Bureau of Statistics of China. The cross-sectional sample of 10,043 households was gathered in August 2009. The data were collected from across 19 Chinese provinces with different income levels. (22) The data samples and questionnaires are available upon request. The means, standard deviations, minimum and maximum values, frequency distributions and other descriptive statistics of the variables used in the regression are computed and presented in Table 1.

More than half of the participants (68.78 per cent) came from relatively poor provinces such as Inner Mongolia, Heilongjiang, Anhui, Guangxi, etc. The rest (31.22 per cent) came from relatively well-off areas, including Beijing, Tianjin, Shanghai, Jiangsu, Guangdong, etc. The occupations of the participants included managers, specialists, clerical workers, factory operators, farmers and so on, and 54.37 per cent of them worked in state-owned or collective firms with the remaining 45.63 per cent in the private sector. There were 6,634 (66.06 per cent) males and 3,409 (33.94 per cent) females. The participants' age ranged from 22 to 74 years (mean = 43.97, SD = 8.45). Over 90 per cent were married (94.32 per cent), with the rest being single, divorced or others. Their average work experience was 23.09 years (SD = 9.76). Almost half of the participants (40.33 per cent) attained college or a higher education level. The average annual income of the employed workers was RMB19,809.54 (SD = 18,070.10) and 64.94 per cent of them earned below-average income.

Our dependent variable was current daily household consumption. Participants were asked to indicate to what degree their consumption was affected by the GFC. The scale ranged from "1" to "5": the higher the score, the more his or her consumption was affected. In order to examine how the GFC affected consumption, the questionnaire in the survey was constructed by deliberately allowing the GFC to be "one period" before consumption in order to frame the questionnaire with a time lag: for example, "Compared with before the GFC, how has your current consumption changed?"

The independent psychological variables were measured as follows. To measure mental accounting, we designed the question on a five-point scale with the higher score indicating a larger change in income (and wealth). A score of "3" meant "no change" whereas "1" denoted "decreased" and "5" denoted "increased". We asked the participants in the questionnaire how their current income and current wealth had changed. On the emotional antecedent, anxiety was measured on a five-point scale. The lowest score "1" indicated "not affected by the GFC" and the highest score "5" denoted "very much affected by the GFC". The same measurement scale applied to the variable "social development optimism". On job insecurity perception, the participants were asked to indicate the extent to which their job insecurity perception was affected (see Greenhalgh and Rosenblatt). The scales used were the same as those for measuring consumption. A score of "5" indicated that job insecurity had gone up while "1" indicated that it had decreased.

MODELS AND RESULTS

Our regression function is expressed as:

[DELTA]Consumption = f([DELTA]Income, [DELTA]Wealth, [DELTA]Psychological Variables, Demographics, Occupation Dummies, Provincial Dummies), where [DELTA] denotes change.

Due to the discrete nature of the data, ordered logit and ordered probit regressions were both employed. Demographic variables, occupation dummies and provincial dummies were also used as independent variables to account for other factors impacting consumption and to act as control variables. The empirical results are presented in Table 2.

Table 2 shows that income and wealth (mental accounting) were highly and significantly correlated with consumption. Consistent with the behavioural life-cycle theory originally proposed by Shefrin and Thaler, (23) we observed a higher propensity to consume with respect to income but relatively lower propensity to consume with respect to wealth. We also found that the consumption of unemployed consumers was less correlated with changes in wealth. For the employed, a change in income was positively related to a change in consumption. A change in wealth was also positively related to a change in consumption but to a much lesser extent than the change in income. For the unemployed, a change in income was positively associated with a change in consumption. However, for the unemployed, a change in wealth was negatively related to a change in consumption: when their wealth increased, and in the presence of uncertainty wrought by the crisis, they consumed less. On the corollary, our result implies that the unemployed save more under economic uncertainty even if they become wealthier. It is of interest in our analysis that the behavioural life-cycle theory seemed to be more applicable to the employed than unemployed workers.

Table 2 also shows that changes in anxiety, which was deliberately framed in the survey as being a result of the GFC, was a significant and important variable relating to consumption. We found that anxiety was negatively correlated with consumption for the employed only: the unemployed were not significantly affected. Our results support Caplin and Leahy's proposition of incorporating anticipatory emotion in a decision model. In this article, we extend Caplin and Leahy's proposition by examining the relation between anxiety and consumption (rather than expected utility). Caplin and Leahy did not distinguish the consumption pattern between the employed and unemployed. Our new finding shows that consumption of the unemployed was not affected by anxiety emanating from an economic crisis.

On the contrary, optimism about social development was significantly correlated to the consumption of unemployed workers but not the employed. Unemployed people, such as retirees, were probably concerned about the provision of social safety nets (formal and informal) on which their incomes depend. Social developments which incorporated these types of provisions might enhance their optimism which then helped raise their personal consumption.

The GFC may also generate the perception of job insecurity among the Chinese workers. As expected, consumption was found to be sensitive to changes in job insecurity perception only among the employed workers. Table 2 also shows that consumption in China during the GFC was not significantly affected by most of the demographics, occupations and province dummies selected for our regressions.

For further analysis, we constructed the marginal effects (Table 3) which compute and compare the sizes of the various coefficients. We observed that for the Chinese consumers, changes in income appeared to be the most important variable correlating with their consumption. However, the importance of psychological variables should not be underestimated. In many cases, their marginal effects were similar to those of changes in wealth. The combined effect of the psychological variables tested in this article was around one-third that of the changes in income.

CONCLUSIONS AND IMPLICATIONS

This article demonstrates that consumption is significantly correlated to the different psychological states emanating from the GFC. The results varied across the unemployed and employed consumers. Hence, it is crucial for China to explore practical ways to reverse the decline in consumption wrought by an economic downturn.

Unlike the United States and many other European countries, social welfare is relatively low in China. It seems appropriate for China to introduce more savings deposit protection schemes, social security and pensions, unemployment insurance, temporary government assistance and other possible public policy measures, especially in times of a financial crisis, to protect income. These measures will protect income during economic slowdowns and may reduce different psychological stresses which, as shown in our empirical models, will also help protect consumption. Social stability may be sustained once consumer welfare is sufficiently safeguarded.

Despite China's economic success, private consumption has not been a key driver of growth. Rather, much of the government expenditure has been channelled into investment projects, notably infrastructure to expand domestic consumption in China via income and other stabilisation programmes, which psychologically or otherwise, help reduce its reliance on exports. This will also be beneficial in addressing a macroeconomic criticism of the current growth model, which has resulted in growing trade imbalances between China and the US and has potentially contributed to the global financial crisis. (24)

ACKNOWLEDGEMENTS

This research was supported by a research grant from Lingnan University (grant no. DR09D4). We wish to thank the reviewers for their valuable suggestions which helped to improve the quality of this article. Any remaining errors are ours alone.

(1) Stijn Claessens, Ayhan Kose and Marco Terrones, "What Happens During Recessions, Crunches and Busts", Economic Policy 26 (2009): 653-700.

(2) For a review, see Barry Levy and Victor Sidel, "The Economic Crisis and Public Health", Social Medicine 4, no. 2 (2009): 82-7.

(3) F. Allen and D. Gale, Understanding Financial Crisis (Oxford: Oxford University Press, 2007).

(4) See Richard Thaler, "Psychology and Savings Policies", American Economic Review 84, no. 2 (1994): 186-92.

(5) George Loewenstein, Weber, E. Hsee and N. Welch, "Risk as Feeling", Psychological Bulletin 127, no. 2 (2001): 267-86.

(6) Angus Deaton, Understanding Consumption (Oxford: Oxford University Press, 1992), p. 1.

(7) Hersch M. Shefrin and Richard Thaler, "The Behavioral Life-Cycle Hypothesis", Economic Inquiry 26, no. 4 (1988): 609-43.

(8) Shefrin and Thaler, "The Behavioral Life-Cycle Hypothesis".

(9) A. Caplin and J. Leahy, "Psychological Expected Utility Theory and Anticipatory Feelings", The Quarterly Journal of Economics 116, no. 1 (2001): 55-79.

(10) Stevan Hobfoll, "Conservation of Resources: A New Attempt at Conceptualizing Stress", American Psychologist 44, no. 3 (1989): 513-24.

(11) Also quoted by Unal-Karaguven and M Hulya, "Psychological Impact of an Economic Crisis: A Conservation of Resources Approach", International Journal of Stress Management 16 (2009): 177-94.

(12) Herbert Simon, "Rationality in Psychology and Economics", Journal of Business 59, no. 4 (1986): 209-24.

(13) George Loewenstein, "Emotions in Economic Theory and Economic Behavior", American Economic Review: Papers and Proceedings 90, no. 2 (2000): 426-43.

(14) Nico Frijda, Anthony Manstead and Ben Sacha, Emotions and Beliefs: How Feelings Influence Thoughts? (Cambridge, UK: Cambridge University Press, 2000).

(15) Daniel Kahneman, "Maps of Bounded Rationality: Psychology for Behavioral Economics", American Economic Review 93, no. 5 (2003): 1449-75.

(16) A. Bachara and A. Damasio, "The Somatic Marker Hypothesis: A Neural Theory of Economic Decision", Games and Economic Behavior 52, no. 2 (2005): 336-72.

(17) Melissa Finucane, Michael Alhakami, Paul Slovic, and S. Johnson, "The Affect Heuristics in Judgments of Risks and Benefits", Journal of Behavioral Decision Making 13 (2000): 1-17.

(18) Paul Slovic, Ellen Peters, Melissa Finucane and Donald MacGregor, "Affect, Risk and Decision Making", Health Psychology 24 (2005): 35-40.

(19) Many types of hazardous events or activities, such as nuclear power, diving, racing, pandemic, etc., were listed; refer to Paul Slovic, "Perception of Risk", Science 236, no. 4799 (1987): 280-5. This article extends Slovic (1987) to the case of a financial crisis.

(20) Leonard Greenhalgh and Zehava Rosenblatt, "Job Insecurity: Toward Conceptual Clarity", Academy of Management Review 9, no. 3 (1984): 438-48.

(21) Greenhalgh and Rosenblatt, "Job Insecurity: Toward Conceptual Clarity".

(22) The 19 provinces or counties are Beijing, Tianjin, Nei Monggol, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Guizhou, Yunnan, Shaanxi, and Ningxia.

(23) Shefrin and Thaler, "The Behavioral Life-Cycle Hypothesis".

(24) Eswar Prasad, "Effects of the Financial Crisis on the US-China Economic Relationship", Cato Journal 29, no. 2 (2009): 223-35.

Jan P Voon (jvoon@ln.edu.hk) is Associate Professor of Economics at Lingnan University. He received his PhD in Economics at La Trobe University. His research interests include behavioural economics and the Chinese economy.

Zhang Ruifang (Ammafrz@gmail.com) is a Master's candidate in the Department of Psychology at Peking University. She obtained her BA in psychology from Beijing Normal University. Her research interests include industrial and organisational psychology, job insecurity and organisational justice.

TABLE 1
DESCRIPTIVE STATISTICS (EMPLOYED--6,993; UNEMPLOYED--3,050)

               Mean (SD)

Dependent      Employed        Unemployed
Variable:

Consumption    2.94 (1.03)     3.05 (0.98)

Independent
Variable:

Wealth         2.39 (0.91)     2.58 (0.83)
Income         2.54 (0.88)     2.76 (0.84)
Anxiety        3.19 (0.85)     3.09 (0.84)
Optimism       3.64 (0.76)     3.72 (0.72)
Job Security   2.77 (0.63)     2.82 (0.58)

Demographic
Variables

Age            43.97 (8.45)    61.15 (10.13)
Total Annual   19809 (18070)   11209 (8551)
  Income
Work           23.09 (9.76)    41.16 (9.98)
  Experience
  (years)

               Min

Dependent
Variable:

Consumption    1

Independent
Variable:

Wealth         1
Income         1
Anxiety        1
Optimism       1
Job Security   1

Demographic    Employed   Unemployed
Variables

Age            21.08      19.67
Total Annual   600        30
  Income
Work           0          1
  Experience
  (years)

               Max

Dependent
Variable:

Consumption    5

Independent
Variable:

Wealth         5
Income         5
Anxiety        5
Optimism       5
Job Security   5

Demographic    Employed   Unemployed
Variables

Age            73.92      61.15
Total Annual   41720      144618
  Income
Work           56         68
  Experience
  (years)

             Level        Label                  Frequency (%)

                                            Employed      Unemployed

Gender       0            Male            5024 (71.84)   1610 (52.79)
             1            Female          1969 (28.16)   1440 (47.21)

Marriage     0            Else             397 (5.68)    405 (13.28)
             1            Married         6596 (94.32)   2645 (86.72)

Education    1            Senior          3558 (50.88)   2235 (73.28)
                          Middle
                          School
                          and below
             2            Technical       2222 (31.77)   631 (20.69)
                          School
                          & Junior
                          College
             3            Undergraduate   1213 (17.35)    184 (6.03)
                          and above

Occupation   Occ_1        Managers         384 (5.49)
             Occ_2        Specialist      1649 (23.58)
             Occ_3        Clericals       2191 (31.33)
             Occ_4        Business &      1185 (16.95)
                          Service
             Occ_5        Farmers          35 (0.50)
             Occ_6        Operators       1272 (18.19)
             Occ_7        Army Men         14 (0.20)

Province     Pro_1        Tianjin          336 (4.80)     264 (8.66)
             Pro_2        Inner            310 (4.43)     85 (2.79)
                          Mongolia
             Pro_3        Jilin            322 (4.60)     121 (3.97)
             Pro_4        Heilongjiang     236 (3.37)     164 (5.38)
             Pro_5        Shanghai         560 (8.01)    340 (11.15)
             Pro_6        Jiangsu          200 (2.86)     100 (3.28)
             Pro_7        Zhejiang         597 (8.54)     298 (9.77)
             Pro_8        Anhui            362 (5.18)     137 (4.49)
             Pro_9        Fujian          782 (11.18)     211 (6.92)
             Pro_10       Hubei            421 (6.02)     170 (5.57)
             Pro_11       Hunan            437 (6.25)     262 (8.59)
             Pro_12       Guangdong        662 (9.47)     130 (4.26)
             Pro_13       Guangxi          283 (4.05)     115 (3.77)
             Pro_14       Chongqing        429 (6.13)     212 (6.95)
             Pro_15       Guizhou          54 (0.77)      31 (1.02)
             Pro_16       Yunnan           204 (2.92)     107 (3.51)
             Pro_17       Shanxi           228 (3.26)     71 (2.33)
             Pro_18       Ningxia          145 (2.07)     50 (1.64)
             Comparison   Beijing          425 (6.08)     182 (5.97)
             Group

TABLE 2
EFFECTS OF PSYCHOLOGY ON CONSUMPTION: ORDERED LOGIT VERSUS ORDERED
PROBIT MODEL (EMPLOYED--6,993; UNEMPLOYED--3,050)

                                                    Ordered Logit

                                                   Employed (6993)

Mental Accounting     Wealth                         .081 (.031) **
                      Income                         .516 (.035) ***
Emotion               Anxiety                       -.071 (.027) ***
Optimism/Pessimism    Optimism with                  .007 (.030)
                      Social Development
Perception            Job Security                   .116 (.042) **
Demographic Control   Gender                         .020 (.052)
  Variable            Marital Status                -.034 (.099)
                      Education Level               -.022 (.018)
                      Total-Income                   .000 (.000)
                      Age                            .007 (.006)
                      Work Experience               -.006 (.005)
Dummy Coding          Occ_1 (Managers)              -.162 (.160)
  (Occupation)        Occ_2 (Specialist)             .006 (.135)
                      Occ_3 (Clericals)             -.045 (.133)
                      Occ_4 (Business & Service)    -.053 (.136)
                      Occ_5 (Farmers)               -.212 (.345)
                      Occ_6 (Operators)             -.189 (.137)
                      Occ_7 (Army Men)             -1.1   (.451) **
Dummy Coding          Pro_1 (Tianjin)                .109 (.134)
  (Province)          Pro_2 (Inner Mongolia)        -.123 (.135)
                      Pro_3 (jilin)                 -.126 (.135)
                      Pro_4 (Heilongjiang)          -.068 (.155)
                      Pro_5 (Shanghai)               .547 (.118) ***
                      Pro_6 (Jiangsu)                .153 (.158)
                      Pro_7 (Zhejiang)               .221 (.116) *
                      Pro_8 (Anhui)                 -.003 (.131)
                      Pro_9 (Fujian)                 .085 (.110)
                      Pro_10 (Hubei)                 .004 (.128)
                      Pro_11 (Hunan)                 .123 (.127)
                      Pro_12 (Guangdong)            -.125 (.116)
                      Pro_13 (Guangxi)               .032 (.142)
                      Pro_14 (Chongqing)             .085 (.127)
                      Pro_15 (Guizhou)               .248 (.306)
                      Pro_16 (Yunnan)               -.048 (.166)
                      Pro_17 (Shanxi)                .621 (.155) ***
                      Pro_18 (Ningxia)               .284 (.179)

                                                     Ordered Logit

                                                   Unemployed (3050)

Mental Accounting     Wealth                         -.107 (.061) *
                      Income                          .633 (.065) ***
Emotion               Anxiety                        -.059 (.047)
Optimism/Pessimism    Optimism with                   .129 (.055) **
                      Social Development
Perception            Job Security                    .001 (.082)
Demographic Control   Gender                          .004 (.089)
  Variable            Marital Status                  .324 (.120) ***
                      Education Level                -.024 (.028)
                      Total-Income                    .000 (.000)
                      Age                             .004 (.010)
                      Work Experience                 .003 (.010)
Dummy Coding          Occ_1 (Managers)
  (Occupation)        Occ_2 (Specialist)
                      Occ_3 (Clericals)
                      Occ_4 (Business & Service)
                      Occ_5 (Farmers)
                      Occ_6 (Operators)
                      Occ_7 (Army Men)
Dummy Coding          Pro_1 (Tianjin)                 .209 (.194)
  (Province)          Pro_2 (Inner Mongolia)         -.393 (.296)
                      Pro_3 (jilin)                  -.349 (.257)
                      Pro_4 (Heilongjiang)            .149 (.280)
                      Pro_5 (Shanghai)                .479 (.185) **
                      Pro_6 (Jiangsu)                 .343 (.256)
                      Pro_7 (Zhejiang)                .080 (.200)
                      Pro_8 (Anhui)                   .289 (.240)
                      Pro_9 (Fujian)                  .231 (.203)
                      Pro_10 (Hubei)                 -.081 (.227)
                      Pro_11 (Hunan)                  .368 (.206) *
                      Pro_12 (Guangdong)              .087 (.244)
                      Pro_13 (Guangxi)               -.054 (.264)
                      Pro_14 (Chongqing)              .315 (.209)
                      Pro_15 (Guizhou)               -.083 (.785)
                      Pro_16 (Yunnan)                 .030 (.261)
                      Pro_17 (Shanxi)                 .331 (.310)
                      Pro_18 (Ningxia)                .586 (.416)

                                                    Ordered Probit

                                                   Employed (6993)

Mental Accounting     Wealth                         .036 (.017) **
                      Income                         .271 (.019) ***
Emotion               Anxiety                       -.044 (.015) ***
Optimism/Pessimism    Optimism with                  .003 (.030)
                      Social Development
Perception            Job Security                   .045 (.023) **
Demographic Control   Gender                         .003 (.030)
  Variable            Marital Status                -.025 (.056)
                      Education Level               -.009 (.011)
                      Total-Income                   .000 (.000)
                      Age                            .004 (.004)
                      Work Experience               -.004 (.003)
Dummy Coding          Occ_1 (Managers)              -.084 (.091)
  (Occupation)        Occ_2 (Specialist)            -.003 (.077)
                      Occ_3 (Clericals)             -.013 (.075)
                      Occ_4 (Business & Service)    -.032 (.077)
                      Occ_5 (Farmers)               -.157 (.195)
                      Occ_6 (Operators)             -.096 (.077)
                      Occ_7 (Army Men)              -.629 (.290) **
Dummy Coding          Pro_1 (Tianjin)                .076 (.079)
  (Province)          Pro_2 (Inner Mongolia)        -.044 (.080)
                      Pro_3 (jilin)                 -.032 (.079)
                      Pro_4 (Heilongjiang)           .003 (.088)
                      Pro_5 (Shanghai)               .318 (.069) ***
                      Pro_6 (Jiangsu)                .108 (.091)
                      Pro_7 (Zhejiang)               .140 (.068) **
                      Pro_8 (Anhui)                  .010 (.077)
                      Pro_9 (Fujian)                 .065 (.065)
                      Pro_10 (Hubei)                 .023 (.074)
                      Pro_11 (Hunan)                 .075 (.074)
                      Pro_12 (Guangdong)            -.047 (.067)
                      Pro_13 (Guangxi)               .035 (.083)
                      Pro_14 (Chongqing)             .076 (.073)
                      Pro_15 (Guizhou)               .107 (.161)
                      Pro_16 (Yunnan)                .028 (.091)
                      Pro_17 (Shanxi)                .343 (.088) ***
                      Pro_18 (Ningxia)               .152 (.104)

                                                    Ordered Probit

                                                   Unemployed (3050)

Mental Accounting     Wealth                         -.052 (.033)
                      Income                          .325 (.035) ***
Emotion               Anxiety                        -.027 (.027)
Optimism/Pessimism    Optimism with                   .080 (.031) **
                      Social Development
Perception            Job Security                   -.026 (.043)
Demographic Control   Gender                          .004 (.051)
  Variable            Marital Status                  .068 (.038) *
                      Education Level                -.013 (.016)
                      Total-Income                    .000 (.000)
                      Age                             .002 (.006)
                      Work Experience                 .002 (.006)
Dummy Coding          Occ_1 (Managers)
  (Occupation)        Occ_2 (Specialist)
                      Occ_3 (Clericals)
                      Occ_4 (Business & Service)
                      Occ_5 (Farmers)
                      Occ_6 (Operators)
                      Occ_7 (Army Men)
Dummy Coding          Pro_1 (Tianjin)                 .103 (.113)
  (Province)          Pro_2 (Inner Mongolia)         -.227 (.174)
                      Pro_3 (jilin)                  -.156 (.146)
                      Pro_4 (Heilongjiang)            .130 (.154)
                      Pro_5 (Shanghai)                .258 (.108) **
                      Pro_6 (Jiangsu)                 .180 (.147)
                      Pro_7 (Zhejiang)                .047 (.117)
                      Pro_8 (Anhui)                   .195 (.140)
                      Pro_9 (Fujian)                  .161 (.119)
                      Pro_10 (Hubei)                 -.068 (.130)
                      Pro_11 (Hunan)                  .196 (.119)
                      Pro_12 (Guangdong)              .095 (.139)
                      Pro_13 (Guangxi)                .008 (.151)
                      Pro_14 (Chongqing)              .192 (.120)
                      Pro_15 (Guizhou)               -.010 (.389)
                      Pro_16 (Yunnan)                 .013 (.147)
                      Pro_17 (Shanxi)                 .190 (.173)
                      Pro_18 (Ningxia)                .387 (.250)

Notes: For wealth, income and consumption, it is the subjective
change in variable between prior to the crisis and the time of
the survey (1 = not affected by crisis and 5 = very much affected
by crisis). For the psychological variables, it is (1 = decreased
to 5 = increased, and 3 = no change). *, **, and *** denote
statistical significance at 10%, 5% and 1% level, respectively.

TABLE 3
EFFECTS OF PSYCHOLOGY ON CONSUMPTION ON ORDERED LOGIT:
MARGINAL EFFECTS

                               Scale Category

                     1               2               3

                 E       U       E       U       E       U

Wealth         .0054   .0056   .0127   .0148   .0023   .0023
Income         .0348   .0331   .0812   .0875   .0145   .0136
Anxiety        .0049   .0031   .0114   .0082   .0020   .0013
Optimism       .0004   .0073   .0010   .0192   .0002   .0030
Job Security   .0078   .0000   .0182   .0001   .0033   .0000

                       Scale Category

                     4               5

                 E       U       E       U

Wealth         .0116   .0177   .0042   .0050
Income         .0746   .1045   .0269   .0298
Anxiety        .0104   .0098   .0038   .0028
Optimism       .0009   .0229   .0003   .0065
Job Security   .0167   .0001   .0060   .0000

Note: E and U denote, respectively, employed and unemployed.