آیا نرخ بیکاری نرخ طلاق را تحت تاثیر قرار می دهد؟ تجزیه و تحلیل از داده های دولت در 1960-2005
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|37131||2011||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Social Science Research, Volume 40, Issue 3, May 2011, Pages 705–715
Abstract We analyzed data from 50 states and the District of Columbia from 1960 to 2005 to study how the unemployment rate and the divorce rate are related. Unemployment is positively related to divorce in a bivariate analysis, but the association is not significant when state and year fixed effects are included in the statistical model. When the sample is divided into time periods, unemployment is negatively and significantly associated with divorce after 1980. These findings provide the strongest support for a “cost of divorce” perspective and suggest that a high rate of unemployment decreases the rate of divorce, net of unobserved time-invariant state characteristics and period (year) trends.
1. Introduction The Great Recession, which began in December, 2007, is widely recognized as the most serious economic crisis since the Great Depression of 1929–1939 (Sum et al., 2009). A key indicator of recessions is the unemployment rate, which reached 10.2% of the US labor force in March 2010—up from 4.6% 3 years earlier (US Bureau of Labor Statistics, 2010). Some economists believe that the US pulled out of the recession in the second half of 2009, whereas others argue that it is still continuing (Cohan, 2010). Irrespective of whether the recession is officially over, recovery will be slow, and the high unemployment rate (at the time of this writing) is likely to persist for several years. Moreover, economists warn that many workers who lost jobs will take a great deal of time—perhaps decades—to recapture their previous level of earnings (Luo, 2009). Do periods of high unemployment destabilize marriage? Although this seems like a strong possibility, the evidence is far from clear about the existence and nature of such an association. Most studies on this topic have used individual-level data to estimate the effects of unemployment on marital stability but, as we note below, these studies are open to multiple interpretations. Moreover, the last study (to our knowledge) that used aggregate-level data to estimate the effects of unemployment on divorce in the US was published a quarter of a century ago (South, 1985). The current study has two goals. The first goal is to determine whether unemployment and divorce rates move in a counter-cyclical or pro-cyclical fashion. To accomplish this goal, we show state-level trends in unemployment and divorce between 1960 and 2005. The second goal is to estimate how unemployment may affect divorce. To address this goal, we conduct regression analyses of state-level data using state and year fixed effects to account for multiple sources of unobserved heterogeneity.
نتیجه گیری انگلیسی
4. Results 4.1. Descriptive trends Fig. 1 shows the divorce rates for each of the 50 states and the District of Columbia. The figure reveals one major outlier. The divorce rate was extremely high in Nevada during the 1960s and then declined substantially in subsequent years. The explanation for this outlier is clear. Prior to the spread of unilateral no-fault divorce in the 1970s, Nevada had unusually lenient divorce laws, and many people traveled there to obtain divorces. Indeed, the high rate of divorce in Nevada is misleading, because many people who obtained divorces were not state residents. Due to Nevada’s atypical pattern, we conducted analyses that included and excluded this state. Excluding Nevada had virtually no impact on the results, presumably because we weighted the data by state population and Nevada is sparsely populated (especially in earlier decades when its divorce rate was most atypical). All of our reported results include Nevada for the sake of completeness. Divorce rate for 50 states and the District of Columbia: 1960–2005. Fig. 1. Divorce rate for 50 states and the District of Columbia: 1960–2005. Figure options Fig. 2 shows the divorce rates again but with Nevada removed. To clarify the figure further, we removed the District of Columbia, which had an atypically high divorce rate in 1975 and 1980. The trends for the remaining 49 states are quite consistent. Although divorce rates are consistently higher in some states than in others, virtually all states followed the same trend over time. That is, divorce rates increased from 1960 to around 1980 and then declined. Divorce rates for 48 states: 1960–2005 (excluding Nevada and the District of ... Fig. 2. Divorce rates for 48 states: 1960–2005 (excluding Nevada and the District of Columbia). Figure options Fig. 3 shows the mean divorce and unemployment rates for states between 1960 and 2005, weighted by population size. The divorce rate shows the expected pattern, with a rapid increase during the 1960s and 1970s and a gradual decline after 1980. The unemployment rate displays a rough correspondence to the divorce rate. During the 1960s, the divorce rate was increasing while the unemployment rate was relatively stable. After this both rates increased in tandem until the 1980s, then declined in tandem during the latter part of the 20th century. The two rates began to diverge again in 2000 and 2005, with the divorce rate continuing to decline while the unemployment rate increased somewhat. Overall, however, the trends appear to be positively correlated. Mean (weighted) divorce and unemployment rates for 50 states and the District of ... Fig. 3. Mean (weighted) divorce and unemployment rates for 50 states and the District of Columbia1960–2005. Figure options 4.2. Main results Table 1 shows the results from several regression models. Model 1 shows the bivariate association between the unemployment rate and the divorce rate. As anticipated from Fig. 3 the bivariate association was positive and significant. The b coefficient indicates that a one point increase in the unemployment rate was associated with a one point increase in the divorce rate. This result is consistent with Hypothesis 1 from the psychosocial stress perspective and suggests that economic hardship associated with unemployment undermines marital stability. Table 1. Regression of state divorce rates on state unemployment rates in the same year (unstandardized coefficients). Model 1. Bivariate Model 2. State and year fixed effects Model 3. Bivariate: 1960–1980 Model 4. State and year fixed effects: 1960–1980 Model 5: Bivariate: 1985–2005 Model 6. State and year fixed effects: 1985–2005 Unemployment 1.073⁎⁎⁎ (0.194) −0.048 (0.184) 1.600⁎⁎⁎ (0.326) 0.310a (0.175) 859⁎⁎⁎ (0.300) −0.380⁎⁎ (0.139) Year dummies 1960 0.000 0.000 1965 2.215⁎⁎⁎ (0.496) 2.271⁎⁎⁎ (0.484) 1970 8.559⁎⁎⁎ (1.085) 8.680⁎⁎⁎ (1.075) 1975 12.417⁎⁎⁎ (0.671) 11.395⁎⁎⁎ (0.622) 1980 14.494⁎⁎⁎ (0.744) 13.615⁎⁎⁎ (0.678) 1985 12.637⁎⁎⁎ (0.885) 0.000 1990 11.351⁎⁎⁎ (0.698) −1.786⁎⁎⁎ (0.454) 1995 10.812⁎⁎⁎ (0.652) −2.282⁎⁎⁎ (0.442) 2000 8.172⁎⁎⁎ (0.792) −5.426⁎⁎⁎ (0.949) 2005 6.932⁎⁎⁎ (0.899) −6.260⁎⁎⁎ (0.996) Constant 12.947 18.753 8.088 18.015 15.372 32.781 R2 0.058 0.815 0.094 0.922 0.040 0.873 N observations 510 510 255 255 255 255 a p = .080. ⁎⁎ p < .01. ⁎⁎⁎ p < .001. Table options Model 2 adds the state and year fixed effects. The regression coefficient was reduced to a nonsignificant value and was close to zero. Fixed-effects models tend to have relatively large standard errors. For this reason, a significant result that becomes nonsignificant when switching to a fixed-effects model may reflect a loss of efficiency rather than a substantive finding. In the present case, however, the standard errors for Models 1 and 2 were similar. Consequently, the most appropriate conclusion would appear to be that the positive association between unemployment and divorce in Model 1 was due to the influence of unmeasured variables (Allison, 2009), although it is not possible to determine what these variables might be. The year dummies (with 1960 serving as the omitted comparison year) revealed the expected trend. That is, the coefficients increased from 1965 through 1980 and declined in subsequent years. (The 50 b coefficients for the state dummy variables are not shown because they are of little substantive interest.) This model yielded an R2 value of .815. Models 3–6 are based on the notion (described earlier) that the association between unemployment and divorce may have changed over time. Model 3, based on the years 1960–1980, revealed a positive and significant association. Model 4 (based on the same years) shows that with state and year fixed effects added to the equation, the b coefficient declined in magnitude but remained positive and marginally significant (p = .08). This result provides modest support for Hypothesis 1 from the psychosocial stress perspective, at least for this period. Model 5, based on the years 1985–2005, also shows a positive and significant association between unemployment and divorce. With state and year fixed effects added to the equation in Model 6, the b coefficient became negative and statistically significant. We compared the b coefficients from models Models 4 and 6 using the formula recommended by Paternoster et al. (1998), and the two coefficients were statistically different from one another (t = 2.96, p < .01). These findings provide support for the Hypothesis 5, which states that the association between unemployment and divorce has changed over time, with the cost of divorce becoming more relevant after 1980. (In alternative specifications, we split the observations at various years other than 1980. The results yielded the same conclusions as those shown in Table 1. That is, the association between unemployment and divorce was positive in earlier years and negative in more recent years.) As we described earlier, the cost of divorce perspective suggests that the negative association between unemployment and divorce should be most prominent when both rates are measured in the same year (as in Table 1). The psychosocial stress perspective, however, holds that a positive association is most likely to emerge when the divorce rate is measured in the years following the measurement of unemployment. Table 2 shows the results of regression analyses in which the divorce rate was measured 1 year, 2 years, and 3 years after the year in which unemployment was measured. (For example, we regressed divorce in 1961, 1962, and 1963 on unemployment in 1960.) For convenience, the results of the analysis in which unemployment and divorce are measured in the same year (from Table 1) also are included in the first row. The b coefficients for state and year dummies are excluded for ease of inspection. Table 2. Regression of state divorce rates in yeart through yeart + 3 on state unemployment rates in year (unstandardized coefficients). Model 1. Bivariate Model 2. State and year fixed effects Model 3. State and year fixed effects: 1960–1980 Model 4. State and year fixed effects: 1985–2005 Divorcet 1.073⁎⁎⁎ (0.194) −0.048 (0.184) 0.310a (0.175) −0.380⁎⁎ (0.139) Divorcet+1 1.019⁎⁎⁎ (0.208) −0.098 (0.194) 0.082 (0.167) −0.216 (0.177) Divorcet+2 0.800⁎⁎⁎ (0.219) −0.261 (0.254) −0.005 (0.187) −0.073 (0.156) Divorcet+3 0.831⁎⁎ (0.207) −0.199 (0.207) −0.063 (0.176) −0.127 (0.154) N observations 510 510 255 255 Note: Each coefficient comes from a separate regression analysis. *p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001. Table options The bivariate associations (shown in Model 1) were positive and significant, irrespective of whether both variables were measured in the same year or whether the divorce rate was measured 1, 2, or 3 years after the unemployment rate. Contrary to predictions from the psychosocial stress perspective, however, the b coefficients became weaker rather than stronger over time. Moreover, the b coefficients from Model 2, which included state and year fixed effects, were negative and not significant. When the sample was divided into the years 1960–1980 and 1985–2005, none of the lagged b coefficients was significant or approached significance. These results provide little support for the psychosocial stress perspective, which assumes that a high level of unemployment increases the subsequent (but not the simultaneous) risk of divorce. (We also used a 4 and 5-year lags and found no significant association between unemployment and divorce.) 4.3. Sensitivity analyses We conducted additional analyses to assess the stability of our findings. In one analysis we replaced the divorce rate (the number of divorces in a state relative to the number of married individuals in the state) with the crude divorce rate (the number of divorces in a state relative to the total state population). The results of this analysis were substantively identical to the results shown in Table 1. These findings support the conclusion that after 1980, unemployment and divorce were negative associated. In another analysis, we replaced the divorce rate with the percentage of separated (but not divorced) adults in the state population. As noted earlier, the psychosocial stress perspective suggests that unemployment rates and divorce rates are not necessarily associated in the same year because most couples experience a period of separation prior to divorce. The unemployment rate and the percentage of separated adults in the population, however, are likely to be positively associated within the same year. This analysis indicated that the unemployment rate was positively and significantly associated with the percentage of separated adults at the bivariate level (b = 1.281, SE = .553, p < .05) but not when state and year fixed effects were incorporated into the model. (No variations over time emerged for this outcome.) Although most methodologists are comfortable with imputing missing data on the dependent variable (e.g., Schafer and Olsen, 1998), some have argued that doing so increases the amount of “noise” in the dependent variable (e.g., von Hippel, 2007). Consequently, we conducted analyses in which cases with missing data on divorce were either included or excluded. We also considered whether weighting data by state population affected the results. In other words, we conducted four sets of analyses (imputation versus listwise deletion by weighting versus not weighting). All four analyses produced results that were substantively identical to those reported earlier.