(1) .
. | Control . | Treatment . | Difference . | |||||
---|---|---|---|---|---|---|---|---|
Attritors (1) . | Non-attritors (2) . | value (3) . | Attritors (4) . | Non-attritors (5) . | value (6) . | Col(1)–Col(4) (7) . | value (8) . | |
Respondent female | 0.70 | 0.85 | 0.79 | 0.83 | 0.41 | −0.09 | 0.20 | |
Respondent age | 54.50 | 56.30 | 0.58 | 61.19 | 58.17 | 0.42 | −6.69 | 0.26 |
Respondent any school | 0.35 | 0.32 | 0.66 | 0.30 | 0.34 | 0.64 | 0.05 | 0.66 |
Widow | 0.42 | 0.41 | 0.83 | 0.50 | 0.44 | 0.48 | −0.08 | 0.54 |
Household size | 4.00 | 4.64 | 3.29 | 4.59 | 0.71 | 0.16 | ||
Per capita expenditures (Kwacha) | 51,533.84 | 43,020.33 | 0.23 | 65,148.46 | 45,520.79 | −13,614.6 | 0.17 | |
No. of livestock owned | 0.76 | 1.35 | 1.47 | 1.38 | 0.89 | −0.71 | 0.25 | |
Generally feel happy | 0.23 | 0.19 | 0.55 | 0.11 | 0.17 | 0.13 | 0.11 | 0.15 |
Life better in 1 year | 0.58 | 0.53 | 0.32 | 0.56 | 0.53 | 0.60 | 0.02 | 0.83 |
Quality-of-life scale (out of 8) | 2.37 | 2.26 | 0.42 | 1.98 | 2.19 | 0.06 | 0.39 | 0.08 |
Low stress scale (out of 20) 1 = most stressed/20 = least stressed | 6.62 | 6.00 | 0.23 | 5.57 | 6.16 | 0.15 | 1.05 | 0.21 |
Not worried about food | 0.18 | 0.15 | 0.39 | 0.14 | 0.15 | 0.84 | 0.04 | 0.51 |
Overall psychological states index | 0.13 | 0.01 | 0.25 | −0.12 | −0.01 | 0.06 | 0.25 | 0.09 |
Waits for 13,000 Kwacha | 0.27 | 0.31 | 0.47 | 0.23 | 0.29 | 0.25 | 0.04 | 0.58 |
Observations |
Notes: Bold denotes differences that alpha = 0.05 level. P values are reported from Wald tests on the equality of means of Treatment and Control for each variable. Standard errors are clustered at the village cluster level
Equation (1) shows our basic empirical specification where |${Y}_{i\mathrm{t}}$| denotes the individual, time-specific outcome of interest and |${\beta}_1$| —the coefficient on the indicator for receiving the transfer ( T i ) in the post periods ( P t )—signifies the average effect of the cash transfer. These effects can be interpreted as causal due to the random assignment to treatment or control status. |${X}_{it}$| is a vector of controls measured at baseline including the main respondent’s age, gender and education and the stratification variable. 7 We then looked at whether the treatment effect on savings and time discounting operated through the psychological states by adding each psychological variable to the models. All standard errors are clustered at the VC level, and we also implement the wild bootstrap ( Cameron, Gelbach & Miller, 2008 ) to account for the fact that we only have 29 clusters.
The main question we address in this paper is the effect of emotional and psychological states on economic decisions at the population level. The empirical challenge is the joint determination of psychological states and economic decisions, and since purposeful randomisation to psychological states on a large scale (outside a lab setting) is infeasible and likely to be unethical, we require a credible non-experimental approach. In this section, we present our alternative identification strategies and their associated assumptions.
We address this problem using an individual fixed-effects (FE) model, where the key assumption for the FE model to provide consistent estimates is that the joint determination of psychological states and economic decisions emanate from an individual characteristic that is fixed over time. In the FE estimation, when there is baseline information we use data from all three waves; when there is no baseline information we use the two post-treatment periods only. Note that the research question and associated identification strategy do not involve using cash transfer receipt status.
The second and potentially more important source of endogeneity stems from time-varying factors that affect both psychological states and economic decisions in the same period, denoted by u t in equation ( 2 ). The myopia displayed by the poor is typically attributed to liquidity constraints and resulting inability to borrow to overcome shocks and smooth consumption. Shocks and liquidity constraints vary across time, will affect inter-temporal choices and also affect psychological outcomes such as stress or emotion.
The fixed effect ( v i t ) is no longer in equation ( 3 ) due to the differencing, and the instrument and error term in equation ( 4 ) is from period (t−2), and no (t−2) factors enter into equation ( 3 ) ( Arellano & Bond, 1991 ). In equation ( 4 ), we exclude assignment to treatment as an instrument, since this would require that assignment to treatment at (t−2) is independent of ∆ u t , the change in the future value of the time-varying endogenous component of equation ( 3 ). Participation in the program (receiving the cash transfer) may allow households to minimise exposure to future shocks and thus reduce the variance in ∆ u t as well as its mean. In addition, we explicitly control for covariate shocks (floods, drought), which can reasonably be treated as exogenous and which are consequential in this setting. Across all waves, about two-thirds of households report experiencing a covariate shock. We also perform robustness checks by explicitly controlling for both idiosyncratic shocks and credit constraints directly in the regressions, though these are not our preferred estimates because they are plausibly endogenous.
First, we identify the average effect of treatment (the cash transfer) on individuals’ time discounting, propensity to have any cash savings and psychological indicators. For outcomes with baseline information, we estimate DD models, pooling all follow-up waves to generate an average treatment effect over the two follow-up periods. For savings, which was only collected at wave 3, we present single difference estimates only. We find that the respondents in the treatment group are 7.1 pp more likely to be willing to wait for a future payout of MWK13,000 compared to those in the control group ( Table 4 , column 2), and treatment leads to a 16-pp increase in the probability that an individual had any savings at endline ( Table 4 , column 1). Furthermore, we see highly significant treatment effects for all of the psychological variables ( Table 4 , columns 3–8). Program beneficiaries are less stressed and more optimistic about their lives currently and projected in the following year. Specifically, receipt of the transfer, on average, increases individuals’ score on the four-item PSS (where 1 is the most stressed and 20 is the least stressed) by a little over one point, increases their likelihood of not worrying about having enough food by 20 pp, increases the likelihood of generally feeling happy as well as believing life will be better in 1 year by 19 pp and increases the score on the quality-of-life scale by about half a point. We also find that treatment is highly predictive of the overall psychological state index as well. Thus, the induction of an exogenous variation in income has noted improvements in respondents’ levels of stress, affect and savings. The results on stress are particularly worth highlighting as there is no prior evidence that a national cash transfer program reduces stress. Stress is seen as an important channel through which poverty affects a wide range of health and anti-social behaviours including violence, alcohol use, suicide and weight gain ( Lupien et al ., 2001 ; Glaser & Kiecolt-Glaser, 2005 ; Peterman et al ., 2017 ).
Means or Psychological Variables by Treatment Status and Wave
. | Control . | Treatment . | ||
---|---|---|---|---|
Outcome . | Baseline . | Combined follow-up . | Baseline . | Combined follow-up . |
Low stress index | 5.89 | 6.62 | 6.12 | 8.08 |
Not worried about food | 0.15 | 0.10 | 0.14 | 0.29 |
Generally feels happy | 0.18 | 0.20 | 0.17 | 0.36 |
Life better in 1 year | 0.53 | 0.55 | 0.53 | 0.75 |
Quality-of-life scale | 2.26 | 2.44 | 2.19 | 2.83 |
Overall psychological index | 0.01 | −0.21 | −0.01 | 0.23 |
. | Control . | Treatment . | ||
---|---|---|---|---|
Outcome . | Baseline . | Combined follow-up . | Baseline . | Combined follow-up . |
Low stress index | 5.89 | 6.62 | 6.12 | 8.08 |
Not worried about food | 0.15 | 0.10 | 0.14 | 0.29 |
Generally feels happy | 0.18 | 0.20 | 0.17 | 0.36 |
Life better in 1 year | 0.53 | 0.55 | 0.53 | 0.75 |
Quality-of-life scale | 2.26 | 2.44 | 2.19 | 2.83 |
Overall psychological index | 0.01 | −0.21 | −0.01 | 0.23 |
Notes : 7551 observations, of which 2517 are from the baseline (2013) and 5034 are from the combined follow-up waves (2014, 2015).
Next, we looked at whether the treatment effect on savings and time discounting operates through the psychological states by adding each psychological variable to the models in columns (1) and (2) of Table 4 —these are shown in Table 5 . For savings in Panel A, the treatment effect from Table 3 diminishes slightly but is still statistically significant and large in all specifications. The largest drop occurs when the overall index is added to the regression (Panel A, column 6) where the treatment effect decreases from 16.6 to 12.1 pp, or about 27%. All the individual psychological variables have a statistically significant association with savings, with ‘never worrying about food’ producing the greatest mediation of the treatment effect as evidenced by the decrease in the estimated effect by 17% from 16.6 pp to 13.7 pp.
Effects of the Malawi SCTP on Savings, Intertemporal Choice and Psychological States
Dependent variable: . | Any savings . | Waits for 13,000 Kwacha . | Low stress index (four-item) . | Not worried about food . | Generally feels happy . | Life better in 1 year . | Quality-of-life scale . | Overall psychological state index . |
---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
Treatment effect | 0.166 (0.03) | 0.071 (0.03) | 1.22 (0.54) | 0.203 (0.04) | 0.186 (0.04) | 0.198 (0.07) | 0.462 (0.11) | 0.467 (0.09) |
Wild bootstrap value | 0.000 | 0.046 | 0.031 | 0.000 | 0.000 | 0.006 | 0.000 | 0.000 |
Estimation | Single Difference | Double Difference | Double Difference | Double Difference | Double Difference | Double Difference | Double Difference | Double Difference |
Observations | 2,517 | 7,551 | 7,551 | 7,550 | 7,551 | 7,331 | 7,551 | 7,551 |
R-squared | 0.082 | 0.010 | 0.093 | 0.058 | 0.049 | 0.072 | 0.117 | 0.099 |
Mean of dep.var. vvar. | 0.03 | 0.30 | 6.00 | 0.15 | 0.18 | 0.53 | 2.23 | −0.001 |
-stat. | 12.84 | 6.26 | 15.97 | 20.08 | 14.61 | 50.59 | 23.37 | 54.34 |
Dependent variable: . | Any savings . | Waits for 13,000 Kwacha . | Low stress index (four-item) . | Not worried about food . | Generally feels happy . | Life better in 1 year . | Quality-of-life scale . | Overall psychological state index . |
---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
Treatment effect | 0.166 (0.03) | 0.071 (0.03) | 1.22 (0.54) | 0.203 (0.04) | 0.186 (0.04) | 0.198 (0.07) | 0.462 (0.11) | 0.467 (0.09) |
Wild bootstrap value | 0.000 | 0.046 | 0.031 | 0.000 | 0.000 | 0.006 | 0.000 | 0.000 |
Estimation | Single Difference | Double Difference | Double Difference | Double Difference | Double Difference | Double Difference | Double Difference | Double Difference |
Observations | 2,517 | 7,551 | 7,551 | 7,550 | 7,551 | 7,331 | 7,551 | 7,551 |
R-squared | 0.082 | 0.010 | 0.093 | 0.058 | 0.049 | 0.072 | 0.117 | 0.099 |
Mean of dep.var. vvar. | 0.03 | 0.30 | 6.00 | 0.15 | 0.18 | 0.53 | 2.23 | −0.001 |
-stat. | 12.84 | 6.26 | 15.97 | 20.08 | 14.61 | 50.59 | 23.37 | 54.34 |
Notes: Each column represents a separate regression. All regressions control for a household experiencing a covariate shock and a set of baseline characteristics including age and schooling of the respondent, and the stratification variable. Standard errors, clustered at the village cluster level, are shown in parentheses below the coefficient. Wild bootstrapping P values for the impact coefficient (1000 reps, H 0 = 0) are shown in Row 2. Dependent variable means are averaged across Treatment and Control at baseline except for any savings which uses endline control means. *** P < 0.01, ** P < 0.05, * P < 0.1.
Results in Panel B are qualitatively similar; all psychological variables have a statistically significant direct association with time discounting, and point estimates are generally the same except for ‘not worried about food’, where the point estimate is reduced by half. We find that with the exception of stress and never worrying about food, the treatment effect is no longer significant in these estimates, indicating that the psychological variables tend to absorb the treatment effect for time discounting. As in Panel A, the largest drop in the estimated treatment effect occurs when we assess the influence of the overall psychological index (Column 12). Robustness checks show that results are consistent when controlling for idiosyncratic shocks and credit constraints, suggesting these are not additional pathways through which the cash transfer influences economic behaviours. See Appendix 3 for these estimates.
We now turn to the causal estimates of stress and affect on the economic decisions using our alternative identification strategies—this analysis is limited to time discounting because savings were only captured in wave three. The first column of Table 6 shows the FE estimates, and these are generally consistent with the results in Table 5 , with three of the five psychological indicators plus the index displaying a significant relationship with time discounting, the exception being stress and ‘not worrying about food’.
Results from the FDIV model are shown in column (2), where the reduced sample size reflects the fact that we only use the last two waves of data in the estimation since the wave 1 value is used as the instrument. The predictive power of the instrument is very strong, and the stage 1 regressions all have highly significant F statistics well above the common benchmark of 10 (complete first-stage regression results are provided in Appendix 4). In this column, only three effects are statistically significant—feeling happy, the quality-of-life scale and the overall psychological index. Point estimates are large; generally feeling happy increases the likelihood of waiting for future money by 11.2 pp, a 36% increase over the baseline mean of 62%. A one standard deviation increase in the psychological index increases the propensity to wait for a future value of MWK13,000 by 24%. Table 4 shows that receiving the cash transfer itself directly affects time discounting. As a robustness check, we limit our sample to just households in the control group in Column 3 to ensure that our results are not driven by the effect of the cash transfer on time-varying unobservables. The results in Column 3 are identical to those on the full sample. The robustness of our results to the exclusion of the transfer recipients also implies that our results are not subject to possible response bias driven by receipt of the transfer, as discussed earlier.
We noted earlier that a shortage of cash due to credit constraints is an important confounder in the relationship between psychological states and economic decisions. In Appendix 3, we have repeated all our analyses with both idiosyncratic shocks and credit constraints as additional covariates with no change in results. 8 These two variables may themselves be endogenous so we do not show them as our preferred estimates, but the fact that they do not affect the main results suggests that the estimates in Table 5 do not reflect access to credit but rather the true causal effect of the psychological variables on economic decisions.
The Effects of Psychological States on Savings and Intertemporal Choice
. | . | |||||
---|---|---|---|---|---|---|
(1) . | (2) . | (3) . | (4) . | (5) . | (6) . | |
Treatment effect | 0.147 | 0.137 | 0.157 | 0.152 | 0.148 | 0.121 |
(0.026) | (0.023) | (0.028) | (0.028) | (0.027) | (0.025) | |
Wild bootstrap value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Low stress index (4-item) | 0.011 (0.002) | |||||
Not worried about food | 0.124 (0.027) | |||||
Generally feels happy | 0.042 (0.013) | |||||
Life better in 1 year | 0.060 (0.015) | |||||
Quality-of-life scale | 0.041 (0.011) | |||||
Overall psychological state index | 0.077 (0.012) | |||||
Estimation | Single difference | Single difference | Single difference | Single difference | Single difference | Single difference |
Observations | 2,517 | 2,517 | 2,517 | 2,464 | 2,517 | 2,517 |
R-squared | 0.090 | 0.102 | 0.084 | 0.088 | 0.090 | 0.101 |
(7) | (8) | (9) | (10) | (11) | (12) | |
Treatment effect | 0.062 | 0.0591 | 0.052 | 0.053 | 0.033 | 0.026 |
(0.033) | (0.033) | (0.033) | (0.032) | (0.032) | (0.03) | |
Wild bootstrap value | 0.065 | 0.083 | 0.124 | 0.109 | 0.312 | 0.396 |
Low stress index (four-item) | 0.007 (0.003) | |||||
Not worried about food | 0.056 (0.018) | |||||
Generally feels happy | 0.099 (0.018) | |||||
Life better in 1 year | 0.098 (0.017) | |||||
Quality-of-life scale | 0.081 (0.009) | |||||
Overall psychological state index | 0.096 (0.012) | |||||
Estimation | Double difference | Double difference | Double difference | Double difference | Double difference | Doubled difference |
Observations | 7,551 | 7,550 | 7,551 | 7,331 | 7,551 | 7,551 |
R-squared | 0.012 | 0.012 | 0.018 | 0.019 | 0.028 | 0.026 |
. | . | |||||
---|---|---|---|---|---|---|
(1) . | (2) . | (3) . | (4) . | (5) . | (6) . | |
Treatment effect | 0.147 | 0.137 | 0.157 | 0.152 | 0.148 | 0.121 |
(0.026) | (0.023) | (0.028) | (0.028) | (0.027) | (0.025) | |
Wild bootstrap value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Low stress index (4-item) | 0.011 (0.002) | |||||
Not worried about food | 0.124 (0.027) | |||||
Generally feels happy | 0.042 (0.013) | |||||
Life better in 1 year | 0.060 (0.015) | |||||
Quality-of-life scale | 0.041 (0.011) | |||||
Overall psychological state index | 0.077 (0.012) | |||||
Estimation | Single difference | Single difference | Single difference | Single difference | Single difference | Single difference |
Observations | 2,517 | 2,517 | 2,517 | 2,464 | 2,517 | 2,517 |
R-squared | 0.090 | 0.102 | 0.084 | 0.088 | 0.090 | 0.101 |
(7) | (8) | (9) | (10) | (11) | (12) | |
Treatment effect | 0.062 | 0.0591 | 0.052 | 0.053 | 0.033 | 0.026 |
(0.033) | (0.033) | (0.033) | (0.032) | (0.032) | (0.03) | |
Wild bootstrap value | 0.065 | 0.083 | 0.124 | 0.109 | 0.312 | 0.396 |
Low stress index (four-item) | 0.007 (0.003) | |||||
Not worried about food | 0.056 (0.018) | |||||
Generally feels happy | 0.099 (0.018) | |||||
Life better in 1 year | 0.098 (0.017) | |||||
Quality-of-life scale | 0.081 (0.009) | |||||
Overall psychological state index | 0.096 (0.012) | |||||
Estimation | Double difference | Double difference | Double difference | Double difference | Double difference | Doubled difference |
Observations | 7,551 | 7,550 | 7,551 | 7,331 | 7,551 | 7,551 |
R-squared | 0.012 | 0.012 | 0.018 | 0.019 | 0.028 | 0.026 |
Notes: Each column represents a separate regression. All regressions control for whether the household experienced a covariate shock as well as a set of baseline characteristics including age and schooling of the respondent, and the stratification variable. Standard errors, clustered at the village cluster level, are shown in parentheses below the coefficient. Wild bootstrapping P values for the impact coefficient (1000 reps, H 0 = 0) are shown in Row 2. *** P < 0.01, ** P < 0.05, * P < 0.1.
The Effects of Psychological States on Intertemporal Choice: FE and FDIV Estimates
Variables . | FE . | . | Wild bootstrap value . | FDIV . | . | Wild bootstrap value . | FDIV (control only) . | . | Wild bootstrap value . |
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | |||||||
Low stress index (four-item) | 0.005 (0.003) | 7,551 | 0.1071 | 0.0002 (0.007) | 5,034 | 0.7287 | −0.000 (0.010) | 2,618 | 0.9910 |
Not worried about food | 0.045 (0.025) | 7,550 | 0.0941 | 0.021 (0.055) | 5,032 | 0.7087 | 0.033 (0.083) | 2,616 | 0.7137 |
Generally feels happy | 0.096 (0.021) | 7,551 | 0.0000 | 0.112 (0.038) | 5,034 | 0.0050 | 0.095 (0.040) | 2,618 | 0.0490 |
Life better in 1 year | 0.088 (0.021) | 7,331 | 0.0000 | 0.054 (0.032) | 4,648 | 0.1031 | 0.052 (0.046) | 2,387 | 0.2983 |
Quality-of-life scale | 0.073 (0.009) | 7,551 | 0.0000 | 0.069 (0.023) | 5,034 | 0.0040 | 0.079 (0.033) | 2,618 | 0.0480 |
Overall psychological state index | 0.082 (0.015) | 7,551 | 0.0000 | 0.074 (0.034) | 5,034 | 0.0410 | 0.067 (0.049) | 2,618 | 0.2833 |
Variables . | FE . | . | Wild bootstrap value . | FDIV . | . | Wild bootstrap value . | FDIV (control only) . | . | Wild bootstrap value . |
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | |||||||
Low stress index (four-item) | 0.005 (0.003) | 7,551 | 0.1071 | 0.0002 (0.007) | 5,034 | 0.7287 | −0.000 (0.010) | 2,618 | 0.9910 |
Not worried about food | 0.045 (0.025) | 7,550 | 0.0941 | 0.021 (0.055) | 5,032 | 0.7087 | 0.033 (0.083) | 2,616 | 0.7137 |
Generally feels happy | 0.096 (0.021) | 7,551 | 0.0000 | 0.112 (0.038) | 5,034 | 0.0050 | 0.095 (0.040) | 2,618 | 0.0490 |
Life better in 1 year | 0.088 (0.021) | 7,331 | 0.0000 | 0.054 (0.032) | 4,648 | 0.1031 | 0.052 (0.046) | 2,387 | 0.2983 |
Quality-of-life scale | 0.073 (0.009) | 7,551 | 0.0000 | 0.069 (0.023) | 5,034 | 0.0040 | 0.079 (0.033) | 2,618 | 0.0480 |
Overall psychological state index | 0.082 (0.015) | 7,551 | 0.0000 | 0.074 (0.034) | 5,034 | 0.0410 | 0.067 (0.049) | 2,618 | 0.2833 |
Notes: The outcome equals 1 if respondent waits 1 month for MWK13,000 instead of taking MWK10,000 now (and 0 otherwise). Each column and row pair present coefficients from a separate regression. Standard errors, clustered at the village cluster level, are shown below the coefficients in parentheses. The identifying instrument in the FDIV regressions is the baseline value of the variable indicated in the first column. Wild bootstrapping P values for the coefficient (1000 reps, H 0 = 0) are shown in for all regressions. Column (3) uses just the control group. *** P < 0.01, ** P < 0.05, * P < 0.1
Insights on the psychology of poverty propose a feedback loop whereby poverty leads to negative emotion and stress, which in turn influence economic behaviours, leading to sub-optimal decisions that effectively perpetuate poverty. Our results from a large sample of ultra-poor households in Malawi support this theory and are consistent with results from laboratory settings demonstrating that psychological well-being directly influences intertemporal choice ( Shah, Mullainathan & Shafir, 2012 ; Mani et al ., 2013 ; Gennetian & Shafir, 2015 ).
The Malawi SCTP, an unconditional cash transfer program, had an impact on all five measures of psychological well-being: happiness, SWB, worrying about food, quality of life and stress. Moreover, these psychological states are predictive of the decision to save money or to wait for money in the future. For example, results show that feeling happy leads to an 11.2-pp increase in waiting for a future value of MWK13,000. Similarly, a unit increase in the Quality of Life scale leads to a 6.9-pp increase in waiting for a future value of MWK13,000, a 23% increase off a mean of 30%, while a standard deviation increase in an overall psychological index increases the likelihood of waiting for a future value of MWK13,000 by 24%. Our findings also suggest that psychological states are not necessarily mediators between the cash transfer and economic decisions. After controlling for the psychological states, the direct treatment effect on time discounting dissipates, but the direct treatment effect on savings persists. Handa, Seidenfeld & Tembo (2020) report effects of a similar unconditional cash transfer administered by the Government of Zambia. In that program, the cash transfer also had a direct effect on both time discounting and SWB, but the direct treatment effect of the cash transfer persisted after controlling for SWB. Together, results from these two countries suggest that the relationship between income support programs, psychological states and economic decisions is complex. While our results support the psychology of poverty hypothesis (by showing that psychological states directly affect economic decisions), the fact that these psychological states do not necessarily mediate the effect of the cash transfer indicates that income support programs alter other features of the lives of beneficiaries that promote positive affect and reduce stress and lead to forward looking economic behaviour.
An important feature of our results is that they derive from a population of ultra-poor cash transfer beneficiaries that are similar in their demographic structure to beneficiaries in other large, national cash transfer programs across sub-Saharan Africa, such as Zambia, Mozambique, Kenya, Ghana and Liberia. For example, the typical beneficiary in the Malawi program has an average age of 58, and the majority are women as the grant is targeted towards labour-constrained households. This is similar to the percent of women beneficiaries in the Kenya Cash Transfer for Orphans & Vulnerable Children (85), the Ghana Livelihood Empowerment against Poverty Program (65), the Zambia Social Cash Transfer (75) and the Mozambique Food Security Program (56) ( Handa et al . 2012 ). 9 Other features of the target population are that just half of beneficiaries are married, the average household size is about 4.5 people and the average consumption level is less than 40 cents per person per day. Another noteworthy feature of our data is that we have multiple measures of psychological states, which is important because not all measures show significant causal effects on time discounting. There is likely to be substantial measurement error in these variables, making it important to capture the underlying concept of emotion and stress through multiple measures in a field setting. In our data, all five measures are statistically correlated with each other. The Quality of Life scale shows the strongest correlation with all other measures, suggesting it might be the preferred scale to use in field settings, as it captures both subjective well-being and life satisfaction dimensions of psychological states. However, the most promising option is to combine the distinct measures into an overall index as we have done to capture the different components of an individual’s psychological state, but this increases the length of the interview.
Research on the causal effect of psychological states on economic decisions in a field setting or at the population level is still in its infancy. Obviously, the random assignment to psychological states on a large scale is not possible. Additional evidence on causal effects could come from natural experiments or exogenous shocks that result in a change in affect or stress, provided the shock does not directly influence economic decisions around inter-temporal choice. Such events, even if they can be identified, are likely to be quite particular and/or affect a unique sub-sample of the population, making it hard to generalise to society as a whole. The strongest evidence, which addresses both internal and external validity, is likely to come from observational studies that exploit panel data techniques coupled with naturally occurring exogenous variation or randomisation in income (as we have in this study) to build a case for causality.
The precise mechanism through which stress and negative affect influence intertemporal behaviours such as saving and time discounting in a field setting is still not clear. Results from the laboratory suggest that stress may weaken cognitive function and lead to bad decisions ( Gennetian & Shafir, 2015 ). Poverty-related stress may also weaken the immune system through increased exposure to latent viral infection, which itself can impede cognitive capacity and inhibit forward-looking behaviour ( Aiello et al ., 2006 ). Negative affect can also have a direct impact on impulse control and the ability to delay gratification ( Loewenstein, 1996 ). Understanding the underlying mechanisms in a field setting remains a key issue on the research agenda. This in turn can improve the design of poverty interventions, including cash transfer programs, to address both the financial and behavioural consequences of poverty that lead to poverty traps. This study provides promising evidence that if programs can relieve stress, worry and negative affect, they may also alter economic decision-making towards more future-oriented and safeguarding behaviours.
Supplementary material is available at Journal of African Economies online.
The Operations Manual of the SCTP defines ultra-poor as a household that is unable to meet the most basic urgent needs, including food and essential non-food items such as soap and clothing, while labour constrained refers to a household with a dependency ratio (ratio of ‘fit to work’ to ‘not fit to work’) of more than three or with no individuals who are fit to work. An individual who is considered not fit to work is someone under the age of 18, over the age of 64 or within the age range 18 to 64 but suffering from a chronic illness or disability or is otherwise unable to work ( Handa et al ., 2014 ).
Survey instruments were reviewed for ethical considerations and approved by the UNC-CH Institutional Review Board and Malawi’s National Commission for Science and Technology, National Committee for Research in Social Sciences and Humanities (UNC IRB Study No. 12-2496; Malawi NCST Study No. RTT/2/20).
Control households were not aware they were receiving the transfer at a later date. The study team additionally checked for anticipation effects in the control group at endline right before those households were scheduled to start receiving payments and found no effects.
Table A.1 in Appendix 1 presents the treatment effect of the cash transfer on all future values in our survey instruments and several other constructed time discounting measures. We find large, statistically significant effects of receipt of the cash transfer on all future values except the largest (MWK40,000). In general we expect, and find, that the cash transfer induces waiting at lower future values.
Of the full 10-item Cohen’s PSS, only four items were included in all three waves of the Malawi SCTP survey. Even though all 10 items were administered in both follow-up rounds, we only use the 4-item scale for study so as to compare the same items over time.
To build the index we first convert each individual indicator to a wave specific z -score, then take the average of the five z -scores.
Our results are robust to the exclusion of these control variables, but we keep them in our models to increase the precision of our estimates.
Credit constraints are measured through a sequence of questions that ask whether the household borrowed money or received items on credit, and if not, whether they could have borrowed if they wanted to. For those who did borrow, we asked whether they would have liked to borrow more at the same price.
The average age of beneficiaries in these four major programs is also similar to the Malawi program. The average age of recipients is 61, 53, 59 and 67 years, respectively, in Ghana, Kenya, Zambia and Mozambique. This information is taken from Handa et al . (2012) and publicly available evaluation reports at the website https://transfer.cpc.unc.edu/ .
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Shira Ben-Sasson, a founder of the Hakhel synagogue in Jerusalem, lights a candle in memory of Hersh Goldberg-Polin, Sept. 1, 2024. (Deborah Danan)
By Deborah Danan September 1, 2024
( JTA ) — JERUSALEM — Three hundred and thirty-two days after Hersh Goldberg-Polin danced in the courtyard next to his Jerusalem synagogue on the holiday of Simchat Torah, more than a thousand people gathered there in grief and prayer to mourn his murder by Hamas terrorists in Gaza.
During the Sunday night vigil, the courtyard railings were lined with oversized yellow ribbons to symbolize advocacy for the hostages, Hapoel Jerusalem soccer flags — the 23-year-old’s favorite team — and posters that read, “We love you, stay strong, survive,” a mantra coined by his mother, Rachel Goldberg-Polin.
Just hours earlier, one of the posters had been hanging over the balcony of the home of Shira Ben-Sasson, a leader of Hakhel, the Goldberg-Polins’ egalitarian congregation in the Baka neighborhood of Jerusalem.
Thoughtful, balanced reporting from the Forward and around the web, bringing you updated news and analysis of the crisis each day.
“We were sure we would take it down when he came home,” Ben Sasson said.
The community wanted to unite while respecting the Goldberg-Polins’ desire for privacy, she said, prompting them to organize the prayer gathering.
“But it’s like a Band-Aid or giving first aid, it’s what you do in an emergency. I don’t know how we go on after this,” she said.
She added that the community, which has a large contingent of English-speaking immigrants, was not prepared for the High Holidays, which begin in about a month. She said, “Seeing his empty seat is hard.”
For Ben-Sasson, who wore a T-shirt bearing the Talmudic dictum “There is no greater mitzvah than the redeeming of captives,” the tragedy is especially painful because, she said, it could have been avoided with a ceasefire agreement that freed hostages.
“Hersh was alive 48 hours ago. We think a deal could have saved him. There is no military solution to this,” she said.
That feeling of bereavement, often mixed with betrayal, pervaded gatherings across Israel on Sunday, as the country struggled with the news that six hostages who may have been freed in an agreement were now dead as negotiations continue to stall. Speakers at protests in Tel Aviv blamed Israeli Prime Minister Benjamin Netanyahu, who himself apologized for not getting the hostages out alive but blamed Hamas for obstructing a deal. The country’s labor union, the Histadrut, has called a national strike on Monday to demand a deal.
Some at the Jerusalem gathering, including the relative of another former hostage, said Netanyahu had chosen defeating Hamas over freeing the captives.
Josef Avi Yair Engel, whose grandson Ofir was released from Hamas captivity in November during that month’s ceasefire deal, expressed shock over Hersh’s murder but said he was not surprised, given the wartime policies of Netanyahu’s government.
“We knew months ago this was going to happen. Bibi’s formula, to dismantle Hamas and return the hostages, wasn’t logical. It’s an either/or situation,” Engel said, referring to Netanyahu by his nickname. “He’s tearing the country apart. I’m afraid that in the coming months there won’t be a state at all.”
Engel said he felt a close bond with Hersh’s father Jon Polin, not only because of their joint activism in the hostage families’ tent outside the Prime Minister’s Residence, but also because of their shared identity as Jerusalemites.
“There aren’t many of us in the hostage circle,” he said. “We’re like family.”
Sarah Mann, who did not know the family personally, said the weekend’s tragedy reminded her of Oct. 7.
“This day has sparks of the seventh, which created numbness and an inability to talk. Just complete shock,” she said.
Part of the reason for that, Mann said, was Rachel, who she described as a “force of faith.” Goldberg-Polin’s mother emerged as the most prominent advocate for the hostages globally and became a symbol in her own right as she crisscrossed the world calling for her son’s freedom.
“Millions of people around the world held onto her. Once that was cut, people’s ability to hold onto faith was knocked out today. But even though this has shattered us, we need to keep holding onto God,” Mann said.
For Susi Döring Preston, the day called to mind was not Oct. 7 but Yom Kippur, and its communal solemnity.
She said she usually steers clear of similar war-related events because they are too overwhelming for her.
“Before I avoided stuff like this because I guess I still had hope. But now is the time to just give in to needing to be around people because you can’t hold your own self up any more,” she said, tears rolling down her face. “You need to feel the humanity and hang onto that.”
Like so many others, Döring Preston paid tribute to the Goldberg-Polins’ tireless activism. “They needed everyone else’s strength but we drew so much strength from them and their efforts, “she said. “You felt it could change the outcome. But war is more evil than good. I think that’s the crushing thing. You can do everything right, but the outcome is still devastating.”
Guy Gordon, a member of Hakhel who moved to Israel from Dublin, Ireland, in the mid-1990s, said the efforts towards ensuring Hersh’s safe return have been an anchor for the community during the war.
“It gave us something to hope for, and pray for and to demonstrate for,” he said. “We had no choice but to be unreasonably optimistic. Tragically it transpired that he survived until the very end.”
Gordon, like many others in the crowd, wore a piece of duct tape marked with the number of days since Oct. 7 — a gesture initiated by Goldberg-Polin’s mother. Unlike on previous days, though, his tape also featured a broken red heart beside the number.
Nadia Levene, a family friend, also reflected on the improbability of Hersh’s survival.
“He did exactly what his parents begged him to do. He was strong. He did survive. And look what happened,” Levene said.
She hailed Rachel Goldberg-Polin’s “unwavering strength and belief in God,” adding, “There were times I lost faith. I suppose I was angry with God. But she just kept inspiring us all to pray, pray, pray.”
Jerusalem resident Leah Silver rejected politicizing the hostages’ deaths.
“Everything turns political so quickly. I came here because I felt that before all the protests, we need to just mourn for a moment and to pray. And show respect for each other,” she said. “We’ve become confused about who the enemy is. It’s very sad.”
But not everyone at the gathering joined in to sing Israel’s national anthem at the closing of the prayer gathering.
“I’m sorry, I can’t sing ‘Hatikvah,’” Reza Green, a Baka resident who did not know the Goldberg-Polins personally, said. “I’m too angry. We shouldn’t be here.”
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COMMENTS
Essay, Pages 6 (1303 words) Views. 5691. If you search up the definition of poverty on google, the first answer you will get is "the state of being extremely poor.". This statement alone is a very broad definition and could relate to many other things. Poverty affects so many people globally and goes further than not having a whole lot of ...
Poverty: Not just a state of mind We have all heard that the poor and minorities need only make better choices - work hard, stay in school, get married, do not have children before they can ...
What is poverty? Can it be measured by income - or the lack of it - or is it a state of mind, asks social worker-turned-writer Bernard Hare, who grew up in a Yorkshire mining family.
This essay is not meant to demean or harm anyone, especially the poor. This is not proven or supported by any psychologist. It is simply an observation living a life of poverty among some of the ...
We should extend the conversation about tackling poverty, but that conversation should not be governed by the belief that poverty in resources is synonymous with poverty of values.
Recently, Housing and Urban Development Secretary Ben Carson said that poverty is a state of mind, and having the right mind-set will let people escape poverty. He was both right and wrong.
Poverty Is Just a State of Mind American dominant culture understands poverty as a defect of character, not a result of a structurally unjust economic system. People who are poor are not poor because they lack money. They are poor because, in America's dominant culture, there is something wrong with them emotionally, morally, subculturally, or behavior-ally. People in poverty in the United ...
Understanding the causes and effects of poverty is crucial in developing effective strategies to combat it. This essay delves into the intricate causes and far-reaching effects of poverty, highlighting the interconnectedness of various factors that perpetuate this global issue.
Just a few weeks ago, Housing Secretary Ben Carson called poverty "a state of mind." At the same time, his budget to help low-income households could be cut by more than US$6 billion next year. This is an example of a simplistic view toward the complex social phenomenon.
Poverty in the United States is a pervasive issue that continues to impact millions of individuals and families across the country. Despite being one of the wealthiest nations in the world, a significant portion of the population struggles to make ends meet, facing challenges such as inadequate access to education, healthcare, and affordable housing. This essay will explore the root causes of ...
The essay focuses on Hare's past, from when he grew up poor in Leeds to adult whose angry and bitter, to eventually letting go of his anger and "spirit of poverty ". The theme in the text is poverty and Hare is trying to emphasize that poverty is a state of mind, by telling about his own past. The essay is written from the authors point ...
Poverty is defined as the state of being unable to fulfill basic needs of human beings. Poverty is the lack of resources leading to physical deprivation. Poor people are unable to fulfill basic survival needs such as food, clothing, shelter. These are the needs of lowest order and assume top priority.
The central problem with Hare's essay and his statements, that poverty is only a state of mind, is that it is psychological. Poverty is big part of humanity, and being physically poor means that your possibilities are restricted. Those people who are stuck in poverty and can't get out, it becomes difficult to reflect on the riches such as ...
People in poverty in the United States are largely viewed as individually responsible for their own economic circumstances, and they must be punished in this view (see Ryan 1976 and Schneider 1999 for representative takes on the issue). This appallingly cruel perspective has a long history in the United States despite its patent falsity, going ...
A Rolling Perspective: Poverty Is A State Of Mind | CPD Blog. During a conversation with Kim, the Costco/Ameriprise lady who was helping me save money, I shared that I was writing a book, The Art of Poverty. "Poverty is a state of mind," I said. She agreed and added: "You may be broke, but you're never poor.".
The HUD secretary Ben Carson said that the "wrong mind-set" leads to poverty. But research suggests cause and effect go the opposite way.
Many people lives in poverty. An example of a man who lived in poverty is Bernard Hare in the text "Poverty is a state of mind " from 2012. The main claim in the text "Poverty is a state of mind" is "Poverty is a state of mind" (l. 320). The ground is "As far as I was concerned, we had warmth, love, shelter, enough to eat ...
During a conversation with Kim, the Costco/Ameriprise lady who was helping me save money, I shared that I was writing a book, The Art of Poverty. "Poverty is a state of mind," I said. She agreed and added: "You may be broke, but you're never poor.". Wise words. But how to stop feeling poor when even able-bodied people feel that way ...
People with the "right mind set" can pull themselves up, Carson said. Many conservatives and the Trump administration say cuts to government benefits could incentivize people to find work.
In conclusion, Bernard Hare's exploration of poverty challenges the simplistic notion of it being merely a state of mind. His life journey becomes a powerful illustration of the intricate interplay between personal choices, external circumstances, and societal factors in shaping one's economic status.
Housing and Urban Development Secretary Ben Carson is standing by his controversial comment that poverty is a "state of mind," but he says that "how a person thinks" is only one component that ...
1. Introduction Standard economic explanations of shortsightedness displayed by individuals living in poverty include liquidity constraints and the urgency to meet immediate needs. The consequences of this myopia and their related behaviours help explain the persistence of poverty among individuals over time and across generations. However, insights from the psychology of poverty could further ...
The central problem with Hare's essay and his statements, that poverty is only a state of mind, is that it is psychological. Poverty is big part of humanity, and being physically poor means that your possibilities are restricted.
JERUSALEM — Three hundred and thirty-two days after Hersh Goldberg-Polin danced in the courtyard next to his Jerusalem synagogue on the holiday of Simchat Torah, more than a thousand people ...