Original Publish Date: 11 July, 2020
Updated on: 27 April, 2021
In this post we try to find how and what the characteristics of constituencies are associated with the dynasticism. We use census data and s and Ashers development data to explore the same.
Asher data provides us information at the assembly constituency level for the the Indian states. The dependent variable corresponds to the number of years rules by a dynast ruler in a particular constituency in a 30 year period starting 1970 to 2001. We limit our analysis to this period because this data is from the populations census and economic census which were conducted in early 2000s. We use the 91 population census as a base given the lack of many variables in the 1971 popultaion census data provided by Francesca.
All the models is a poisson regression regressions. In Asher’s data set all the variables have a urban and rural counter parts. The first one, we have all urban and rural variables in one model. Second and third ones are separate urban and rural models respectively.
| Dependent variable: | |||
| Dynast rule | |||
| (1) | (2) | (3) | |
| pc91_vd_power_supl | -0.692*** | -0.755*** | |
| (0.207) | (0.205) | ||
| el_con91 | 0.00000* | 0.00000 | |
| (0.00000) | (0.00000) | ||
| p_sch_r91 | 0.100 | -0.073 | |
| (0.293) | (0.285) | ||
| p_sch_u91 | -0.010** | -0.010** | |
| (0.005) | (0.005) | ||
| s_sch_u91 | -0.002 | -0.003 | |
| (0.007) | (0.006) | ||
| irr_share91 | -0.262 | -0.400 | -0.150 |
| (0.311) | (0.301) | (0.299) | |
| hosp_r91 | -12.787*** | -13.343*** | |
| (2.235) | (2.250) | ||
| hosp_u91 | 0.027 | 0.030* | |
| (0.018) | (0.018) | ||
| pc91u_td_p_road | 0.0002 | -0.0004 | |
| (0.002) | (0.002) | ||
| pc91_vd_app_pr | 0.260 | 0.291 | |
| (0.279) | (0.278) | ||
| constituency_typeSC | -1.096*** | -1.041*** | -1.099*** |
| (0.130) | (0.129) | (0.129) | |
| p_urban | 0.010*** | 0.009*** | |
| (0.003) | (0.003) | ||
| Prural | -0.010*** | ||
| (0.003) | |||
| Constant | 1.363*** | 1.888*** | 1.455*** |
| (0.230) | (0.234) | (0.224) | |
| Observations | 359 | 359 | 359 |
| Log Likelihood | -1,154.276 | -1,180.365 | -1,161.160 |
| Akaike Inf. Crit. | 2,334.553 | 2,378.730 | 2,338.320 |
| Note: | p<0.1; p<0.05; p<0.01 | ||
We use Francesca’s ac level census data to regress the change in the constituency characteristics against the years ruled by dynast within a 30 year period(1971-2001). In this poisson regression we used sub-regions as controls.
| Dependent variable: | |
| Dynast rule | |
| lit | -0.390*** |
| (0.091) | |
| work | -0.841** |
| (0.346) | |
| agr_work | -0.323 |
| (0.239) | |
| constituency_typeSC | -1.037*** |
| (0.124) | |
| Observations | 394 |
| Log Likelihood | -1,275.333 |
| Akaike Inf. Crit. | 2,572.666 |
| Note: | p<0.1; p<0.05; p<0.01 |
In this section we use the All India GE level dynast data to asses the characteristics of constituencies where dynasts win or contest. This is a probit regression with state and year fixed effects.
| Dependent variable: | ||
| dynast_winner | dynast_candidate | |
| (1) | (2) | |
| turnout | -0.003 | 0.004 |
| (0.005) | (0.004) | |
| enop | -0.324*** | -0.174*** |
| (0.052) | (0.043) | |
| reservationSC/ST | -0.145* | -0.175*** |
| (0.077) | (0.066) | |
| literacy | 0.070 | -0.236 |
| (0.502) | (0.433) | |
| urban_pop | -0.428* | -0.283 |
| (0.228) | (0.198) | |
| muslim_pop | 0.144 | 0.347 |
| (0.264) | (0.227) | |
| sc_st_pop | 0.301 | 0.425 |
| (0.335) | (0.289) | |
| agri_lab | -1.113 | -1.615 |
| (1.194) | (1.057) | |
| Observations | 3,900 | 3,900 |
| Log Likelihood | -1,236.354 | -1,761.517 |
| Akaike Inf. Crit. | 2,560.707 | 3,611.034 |
| Note: | p<0.1; p<0.05; p<0.01 | |
We regressed the wealth score which was calculated from the 20011 census against the number of years ruled by dynast in a constituency from 1977 to 2004.
| Dependent variable: | |
| dyn_rule | |
| wealthscore | 0.039*** |
| (0.006) | |
| constituency_typeSC | -1.471*** |
| (0.243) | |
| Constant | 0.472** |
| (0.213) | |
| Observations | 81 |
| Log Likelihood | -345.751 |
| Akaike Inf. Crit. | 697.502 |
| Note: | p<0.1; p<0.05; p<0.01 |