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3.2. Assessment on change in poverty identification


According to the decision 210/2006/QD-TTg, MOLISA provincial poverty rates were used to calculate scores, with 10 percent of poverty rate equals to 1 point. Statistics from the Finance and Budget Committee of National Assembly indicate that total poverty scores used to allocate capital expenditures for budgetary stability period of 2007-2010 was 117.8 points (data of 2006). It would be 147.2 points, a 31.4-point increase or an approximately increase by 26.6 percent, if GSO provincial poverty rates were used.1

To evaluate how the change in provincial poverty identification affects capital expenditures to provinces, this section will use two measurements on capital expenditures, including (i) differences in per capita capital expenditures allocated to provinces and (ii) differences in capital expenditure shares allocated to provinces.2 According to the measurements, the changes in the measurements show whether provincial poverty rate identification is pro-poor or not. If provincial poverty rate identification brings higher positive change in the measurements, it is more pro-poor, and vice verse. The relationship between MOILSA/GSO provincial poverty rates and differences in per capita capital expenditures distributed to the province is not clearly defined. This relationship is clearly presented in the figure 3 and 4.



Figure 3: GSO provincial poverty rates and difference in per capita capital expenditure between 2006 and 2007


Trend

Source: Consolidation from data provided by the Finance and Budget Committee of National Assembly


Figure 4: MOLISA provincial poverty rates and difference in per capita capital expenditure between 2006 and 2007

Trend

Source: Consolidation from data provided by the Finance and Budget Committee of National Assembly

The figures show that both provincial poverty rate identifications do not clearly present the relationship between provincial poverty rate and difference in per capita expenditure allocated to the provinces. When the trend mainly described the relationships is added, it somehow reveals those relationships. The quadratic trends presented in those figures mean that both MOLISA and GSO provincial poverty rates benefited the rich and poor provinces. The slop of the quadratic trend, however, shows more pro-poor of GSO provincial poverty rates than that of MOLISA in terms of differences in per capita capital expenditures allocated to provinces.

Regression results also show negative relationships between the two variables, and it is presented in the table 8. Using MOLISA provincial poverty rate, its total effect is less than that when using GSO provincial poverty rate, at all poverty rate levels less than 26.8%.3 It means that GSO poverty rate has its larger total effects on difference in per capita capital expenditures than that of MOLISA poverty rate for a cluster of provinces with their poverty rates are less than 26.7%. When provincial poverty rates are, however, larger than 26.8%, total effects of MOLISA provincial poverty rate are larger than that of GSO. This allocation mechanism (shifting from MOLISA to GSO provincial poverty rates) seems to support both rich and poor provinces, but it is more pro-poor the provinces for provinces with less than 26.8% of provincial poverty rate.

Table 8: Regression results of difference in per capita capital expenditures on provincial poverty rates, 2006-2007

Poverty identification


Intercept


Provincial poverty rate Coef.

Square of provincial poverty rate Coef.

R-Squared


GSO

-0.3905***

0.0330***

-0.0005***

0.2322

MOLISA

-0.2106

0.0116

-0.0001*

0.0944

Note: *** means that the coefficients are statistically significant at 1 percent level, ** at 5 percent level, and * at 1% level.

For the budgetary stability period of 2011-2015, regression results show that coefficients of GSO provincial poverty rates are higher than that of MOLISA one (even both coefficient are only statistically significant at 10 percent level). These results imply that GSO provincial poverty rates are more pro-poor than that of MOLISA identification in terms of per capita capital expenditures allocated to the provinces.

Table 9: Regression results of differences in per capita capital expenditures allocated to province on provincial poverty rates, 2010-2011

Poverty identification

Intercept

Provincial Poverty Rate Coefficients

R-Squared

GSO

0.0910232**

0.0007205*

0.002

MOLISA

0.0925208**

0.0007426

0.002

Note: *** means that the coefficients are statistically significant at 1 percent level, ** for 5 percent level, and * for 10% level.

When studying provincial poverty rates and change in shares of capital expenditures distributed to the province, however, a positive relationship between the two variables is obtained. Showing both MOLISA provincial poverty rates and change in share of provincial capital expenditures in a scatter diagram (figure 5), the changes in allocated capital shares of richer/more developed provinces, at least in the top ten provinces, lie under the zero line. It implies that provincial poverty rates seem to support poorer rather than the rich provinces. A similar trend is also found in showing GSO provincial poverty rate and changes in allocated capital shares in a scatter diagram (figure 6). In this case, however, provinces scattering far away from the zero line include most developed provinces, which away down, more developed provinces, which away upward, and poor provinces, which insignificantly above zero line. As an allocation norm, thus, provincial poverty rate seem to support middle-classed and poor provinces, and it does not support the rich provinces.



Figure 5: MOLISA provincial poverty rates and changes in shares of capital expenditures during the budgetary stability period 2011-2015



Note that a change in share of Ho Chi Minh city was -4.3, and it was too large to be included in the scatter, so it was taken out of the graph.

Source: Consolidation from data provided by the Finance and Budget Committee of National Assembly



Figure 6: GSO provincial poverty rates and changes in share of capital expenditures during the budgetary stability period 2007-2010

Note that a change in share of Ho Chi Minh city was -4.3, and it was too large to be included in the scatter, so it was taken out of the graph.

Source: Consolidation from data provided by the Finance and Budget Committee of National Assembly

Regression results show a positive relationship between provincial poverty rates, including MOLISA and GSO identification, and shares of capital expenditures distributed to provinces for both budget stability periods. The result implies that MOLISA provincial poverty rates are less pro-poor than GSO one. In these budgetary stability periods of 2007-2010 and 2011-2015, using GSO provincial poverty rates is more pro-poor than using MOLISA one as an allocation norms of capital expenditures. These results are shown in the tables 10 and 11.

Table 10: Regression results of provincial shares of capital expenditures on poverty rates for 2006-2007

Poverty identification

Intercept

Provincial Poverty Rate Coefficients

Adjusted R Square

GSO

-0.1941***

0.0104***

0.1484

MOLISA

-0.1626**

0.0069**

0.0916

Note: *** means that the coefficients are statistically significant at 1 percent level, ** for 5 percent level, and * for 10% level.

Table 11: Regression results of capital expenditure share allocated to province on provincial poverty rates for 2010-2011

Poverty identification

Intercept

Provincial Poverty Rate Coefficients

R-Squared

GSO

-0.2004139*

0.0119861*

0.0355

MOLISA

-0.1294523 *

0.0091121

0.0178

Note: *** means that the coefficients are statistically significant at 1 percent level, ** for 5 percent level, and * for 10% level.

In the budgetary stability periods, MOLISA provincial poverty rates were used as one of the allocation norms to distribute capital expenditures to the provinces. Using this norm, several findings have been found:



  • When MOLISA provincial poverty rates are used as an allocation norm, the total poverty scores would be lower than that when using GSO. In terms of poverty scores, thus, GSO poverty would be more pro-poor than MOLISA;

  • Regarding to the amount of per capita capital expenditure allocated to the province, provincial poverty rate identification does not support the richest provinces, and do not strongly benefit poorest provinces as expected. The GSO provincial poverty rates seem to be pro-poor for provinces whose poverty rates are less than 26.8%; for the poorest provinces (their annual provinical poverty rates are larger than 26.8%), MOLISA poverty rate is more pro-poor than GSO one.

  • Using changes in share of capital expenditures allocated to the provinces for both budgetary stability period of 2007-2010 and 2011-2015, GSO provincial poverty rates is more pro-poor than using MOLISA one.
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