Does the central bank contribute to the political monetary cycles in Bangladesh?

Abstract

This study aims to explore the impact of elections on the inflationary process in Bangladesh i.e., to observe whether elections have any influence on monetary variables like money supply, exchanges rate and thus inflation. We used quarterly data as the incumbent in Bangladesh must give a maximum of 3 months’ time to the caretaker government to accomplish the election. Our full ARDL estimation suggested that an appreciation of the exchange rate just before the election reduced inflation significantly and an effective way of incumbent’s to gain popularity. We found that current inflation was positively associated (though not significantly) with the previous quarter’s money supply measured by M1. The incumbent took policies to reduce the inflation during election period as voters place more importance in the recent history. We find evidence of political monetary cycles in Bangladesh. Bangladesh Bank’s independence should be given priority so that it can withstand political pressure to stimulate the economy before elections or finance election-related increases in government spending.

Appendices

Appendix A

See Table 6.

Table 6 Summary statistic

1.1 Methodology for arriving quarterly GDP from yearly GDP data

First Step: Sector-wise quarterly weight is derived for each year for the sample period FY80–FY09. The derivation of sector-wise quarterly weight, i.e., agriculture, industry and service sector, for each year as follows:

A. Agriculture sector: Agriculture quarterly weight is derived based on crop production, i.e., Aus, Aman, Boro, and wheat.

$$ {\text{Weight for }}Q1 \, : {\text{Q}}1{\text{w}} = \frac{\text{ACP}}{\text{TCP}} \times 100 $$

where Q1 = July–September, w = weight, ACP = Aus crop production, and TCP = total crop production

$$ {\text{Weight for Q}}2:{\text{ Q}}1{\text{w}} = \frac{\text{AMCP}}{\text{TCP}} \times 100, $$

where, Q2 = October-December, AMCP = Aman crop production.

$$ {\text{Weight for Q}}3:{\text{Q}}3{\text{w}} = \frac{\text{BCP}}{\text{TCP}} \times 100 $$

where Q3 = January-March, BCP = Boro crop production.

$$ {\text{Weight for Q}}4:{\text{Q}}4{\text{w}} = \frac{\text{WCP}}{\text{TCP}} \times 100 $$

where Q4 = April-June, WCP = wheat crop production.

B. Industry Sector: Quarterly weights for Industry sector are derived based on monthly quantum industrial production index:

$$ {\text{Quarterly weight}} = \frac{{\sum\nolimits_{1}^{3} {\text{QIPm}} }}{{\sum\nolimits_{1}^{12} {\text{QIPm}} }} \times 100 $$

C. Service Sector: Quarterly service sector weight is distributed evenly due to lack of quarterly common yardsticks measure. Besides, this sector is less volatile.

Second Step: There are four sub-sectors in agriculture sector. First, yearly agriculture crop sub-sector’s production is decomposed into quarterly as per respective quarterly weight. Then, fishing, forest, and related services, and animal farming sub-sector’s production are decomposed into quarterly evenly. Secondly, sub-sector’s quarterly value is added to get quarterly overall agriculture GDP.

Third Step: Quarterly production value which is decomposed as per above mentioned formula are added to get quarterly GDP for each quarter.

Appendix B: characteristics of money supply

We estimated the trend line of the growth rate of money supply for our sample period. The trend line is negatively slopped. We ran the following trend equation:

$$ \left[ {\text{Growth Rate of Money Supply}} \right]_{t} = {\text{ a}}_{1} + {\text{ a}}_{2} *{\text{Time}} $$

(B1)

See Table 7.

Table 7 Trend regression of growth rate of money supply

The following Figs. 3, 4 and 5 show the scatter plot and trend line and, line plot and trend line of growth rate of money supply and trend line of growth rate of money supply. And the money supply.

Fig. 3

Scatter plot and trend line

Fig. 4

Line plot and trend line

Fig. 5

Money supply

These figures clearly depict the downward trend of growth rate of money supply, which facilitates the negative association between money supply and inflation in Eq. (5).

	Sectoral weight distribution (in %)
	Agriculture					Industry					Service
	Q1	Q2	Q3	Q4	Total	Q1	Q2	Q3	Q4	Total	Q1	Q2	Q3	Q4	Total
FY80	21.00	55.00	18.00	6.00	100.00	22.00	27.00	29.00	22.00	100.00	25	25	25	25	100
FY81	22.00	53.00	18.00	7.00	100.00	19.00	26.00	31.00	24.00	100.00	25	25	25	25	100
FY82	22.00	49.00	21.00	8.00	100.00	24.00	25.00	27.00	24.00	100.00	25	25	25	25	100
FY83	20.00	50.00	23.00	7.00	100.00	23.00	25.00	27.00	25.00	100.00	25	25	25	25	100
FY84	20.00	50.00	21.00	9.00	100.00	21.00	26.00	28.00	25.00	100.00	25	25	25	25	100
FY85	17.00	50.00	24.00	9.00	100.00	23.00	26.00	25.00	26.00	100.00	25	25	25	25	100
FY86	17.00	53.00	23.00	7.00	100.00	26.00	25.00	25.00	24.00	100.00	25	25	25	25	100
FY87	19.00	50.00	24.00	7.00	100.00	25.00	28.00	24.00	23.00	100.00	25	25	25	25	100
FY88	18.00	47.00	29.00	6.00	100.00	25.00	26.00	27.00	22.00	100.00	25	25	25	25	100
FY89	17.00	41.00	35.00	7.00	100.00	23.00	25.00	26.00	24.00	98.00	25	25	25	25	100
FY90	13.00	49.00	33.00	5.00	100.00	24.00	26.00	26.00	24.00	100.00	25	25	25	25	100
FY91	12.00	48.00	34.00	6.00	100.00	24.00	25.00	27.00	24.00	100.00	25	25	25	25	100
FY92	11.00	48.00	35.00	6.00	100.00	25.00	25.00	26.00	24.00	100.00	25	25	25	25	100
FY93	11.00	50.00	34.00	5.00	100.00	24.00	24.00	25.00	27.00	100.00	25	25	25	25	100
FY94	9.00	49.00	35.00	7.00	100.00	23.00	25.00	27.00	25.00	100.00	25	25	25	25	100
FY95	10.00	47.00	36.00	7.00	100.00	22.00	25.00	27.00	26.00	100.00	25	25	25	25	100
FY96	8.00	46.00	38.00	8.00	100.00	25.00	24.00	24.00	27.00	100.00	25	25	25	25	100
FY97	9.00	47.00	37.00	7.00	100.00	26.00	25.00	23.00	26.00	100.00	25	25	25	25	100
FY98	9.00	43.00	39.00	9.00	100.00	26.00	25.00	23.00	26.00	100.00	25	25	25	25	100
FY99	7.00	35.00	48.00	10.00	100.00	25.00	26.00	23.00	26.00	100.00	25	25	25	25	100
FY00	7.00	41.00	44.00	8.00	100.00	25.00	25.00	24.00	26.00	100.00	25	25	25	25	100
FY01	7.00	42.00	44.00	7.00	100.00	26.00	25.00	23.00	26.00	100.00	25	25	25	25	100
FY02	7.00	42.00	46.00	5.00	100.00	25.00	24.00	25.00	26.00	100.00	25	25	25	25	100
FY03	7.00	42.00	46.00	5.00	100.00	25.00	24.00	25.00	26.00	100.00	25	25	25	25	100
FY04	5.00	42.00	47.00	6.00	100.00	25.00	24.00	25.00	26.00	100.00	25	25	25	25	100
FY05	6.00	37.00	53.00	4.00	100.00	25.00	23.00	25.00	27.00	100.00	25	25	25	25	100
FY06	6.00	38.00	50.00	6.00	100.00	25.00	24.00	25.00	26.00	100.00	25	25	25	25	100
FY07	5.50	38.00	52.00	4.50	100.00	26.00	24.00	25.00	25.00	100.00	25	25	25	25	100
FY 08	6.00	36.00	53.00	5.00	100.00	25.00	23.00	26.00	26.00	100.00	25	25	25	25	100
FY 09	5.50	37.00	53.50	4.00	100.00	23.00	25.00	26.00	26.00	100.00	25	25	25	25	100