Capital Stock Calculation

To estimate Equation 4.11, I need to construct time series of capital stocks of electricity and transportation infrastructure. For capital stock calculation I apply perpetual inventory method that takes the following form:

Kt= (1−δ)Kt−1+It−1 (4.12)

where,Kt is the capital stock available at beginning of period t, δis the rate of depreciation,

and It−1is the gross fixed capital formation in periodt−1. Repeated substitutions ofKt−1

in Equation 4.12 yields: Kt = ∞ X i (1−δ)iIt−(i+1) (4.13)

Equation 4.13 suggests that capital stock data can be calculated from gross fixed capital formation data if the initial capital stock K0 is known. Harberger (1978) suggests using

steady state conditions from neoclassical growth literature to obtain initial capital stock.

1

ηE = rX_wXkβlE−wXlβkE

lβqk−rXkβql, and ηT =

rXkβlT−wXlβkT

wXlβqk−rXkβql. Details of derivation of Equation 4.10 is provided in

Since the growth rate of capital equals the growth rate of output in the steady state,K0 can

be calculated from the following formula:

K0 =

I0

gGDP +δ

(4.14)

Harberger (1978) further suggests using three-year average investment and three-year average GNP growth rate to eliminate any short-run fluctuations. Nehru & Dhareshwar (1993) propose an alternative econometric approach for calculatingI0. They run a linear regression

of log of investment over time, and use first period fitted value as I0. I obtain I0 following

Nehru & Dhareshwar (1993)’s approach, and obtain growth rate of GDP by regressing log of GDP on year. Following IMF (2017), different time varying depreciation rates are assumed for Mauritius and Bangladesh. The capital stock calculation equation, therefore,becomes:

Kc,t = (1−δc,t)Kc,t−1+Ic,t−1 (4.15)

Where, subscript c denotes country. IMF (2017) sets different depreciation rates of public capital for low, middle, and high income countries. For all country groups, depreciation in 1960 was set at 2.5%, which remains same for low income countries, and increases to 3.55% and 4.70% respectively for middle and high income countries in 2015. According to The World Bank classification of country and lending groups, Mauritius is an upper middle income country, while Bangladesh is a lower middle income country. In line with IMF (2017), I assume initial depreciation rate of 2.5% for both Bangladesh and Mauritius. I assume that depreciation rate increases to 3.025% for Bangladesh, and 4.125% for Mauritius in 20162_.

2

For Bangladesh, I take average of low income country depreciation rate (2.5%) and middle income country depreciation rate (3.55%). For Mauritius, I take average of middle income country depreciation rate (3.55%) and high income country depreciation rate (4.70%).

Following Gupta et al. (2014), I assume that depreciation rate increases monotonically at a constant rate to reach the assumed depreciation rate in 2016.

4.4

Data

I use data from 1977 to 2016 to estimate elasticities for Mauritius. The interest rate measure is the annual bank rate of Mauritius, which is a simple average of the weighted average yield per annum of 91-day, 182-day, 273-day, and 364-day Government of Mauri- tius Treasury Bills. Since July 2014, Bank of Mauritius has stopped reporting bank rates. For years 2014 to 2016, “weighted average yield on bills accepted at primary auctions” is used as interest rate measure. Data for 1999 to 2016 are obtained from various issues of the Monthly Statistical Bulletin of the Bank of Mauritius. For earlier periods, the end of period (December) bank rates are used, as reported by Jankee (1999) and various World Bank publications. The wage rate measure is average monthly earnings by industrial groups obtained from the Survey of Employment and Earnings in Large (employing 10 or more) Establishments, various issues by Statistics Mauritius. Average monthly earning data, for some earlier years are obtained from the IMF country reports and World Bank publications. From the year 2000 onward, average monthly earnings are reported for more disaggregated industry groups. To construct a consistent series, weighted averages of monthly earnings are calculated, where weight is sectoral employment share in large establishments. Nominal wages are then deflated by the annual CPI to obtain real wages in constant 2006 prices.

Gross domestic fixed capital formation (GDFC) and sectoral GDP data are obtained from the Statistics Mauritius’s “National Accounts Estimate”, various issues, and “Historical Series – National Accounts”. Until 1992, the national accounts of Mauritius was reported for 10 major industry groups. From 1992 to 2005, there were 14 major industry categories, which further increases to 19 categories since 2006. For consistency of sectoral output data,

industry categories are aggregated to 10 initial categories. Details of aggregation is presented in Appendix C. Similarly, GDFC series is also aggregated to generate “Electricity, Gas and Water Supply” (hence forth electricity) and “Transport , Storage and Communications” (hence forth transport and communication) capital stocks for Mauritius. Figure ?? shows trend infrastructure GDFC as share of GDP. Average GDFC in electricity sector is 1.6% of GDP, and average GDFC in transportation and communication sector is 3.6% of GDP. More than 80% of the electricity capital stock, and around 70% of the transport and communication capital stock in 2016 are public capital stocks. Figure 4.3 shows infrastructure capital stocks in Mauritius over time. Average annual growth rate of electricity capital stock is 4.16% for the sample period. Average growth rate was highest during 1996 to 2000 5-year period, and lowest during 2005 to 2010 5-year period. For transport and communication capital stock, average annual growth rate is 3.5% for the sample period. During 1986 to 1990 5-year period, average growth rate was highest, and was lowest during 2010 to 2015 5-year period (Figure 4.5).

For Bangladesh, I use data from 1977 to 2015 to estimate sectoral output and public infrastructure elasticites. Bank rate is used as the measure of interest rate. Bank rate data is obtained from Monthly Economic Trends, various issues, published by the Bangladesh Bank. Wage is measured by the Wage Rate Index (WRI) of the Bangladesh Bureau of Statistics (BBS). Unlike Mauritius data, WRI is a common index for all industry groups. Sectoral output data are also obtained from BBS publication, “GDP of Bangladesh”, various issues. Measures of public infrastructure investment are sector wise annual development program (ADP) expenditures in power, transport, and communication. ADP expenditure data are obtained from Implementation, Monitoring & Evaluation Division (IMED) of the Ministry of Planning, Bangladesh. Trend in sectoral ADP expenditure as share of GDP is presented in Figure 4.2. Average public infrastructure investment as share of GDP in electricity, transport, and communication are 0.57%, 0.74%, and 0.13% respectively. Figure 4.4 shows

public infrastructure capital stock in Bangladesh over time. Average annual growth rate is 4.5% for both electricity, and transport capital stock, and 3.5% for communication capital stock during the sample period. Average growth rate for electricity capital stock is highest during 2010 to 2015 5-year period, and lowest during 1991 to 1995 5-year period. Transport capital stock average growth rate is highest during 1996 to 2000 5-year period, and lowest during 2005 to 2010 5-year period. Average growth rate for communication capital stock is highest during 2001 to 2005 5-year period, and lowest during 2010 to 2015 5 -year period (Figure 4.6).

For sectoral output analysis, I consider 7 aggregated industry groups, which are i) agri- culture, ii) mining, iii) manufacturing, iv) construction, v) trade, vi) finance, and vii) other services. Average sectoral growth rates over 5-year periods for Mauritius and Bangladesh are presented in Table 4.1 and 4.2. The Mauritian economy enjoyed a buoyant average eco- nomic growth in late 1980s, and continued to grow steadily throughout 1990s. The economy slightly slowed down during the first half of the new millennium, but bounced back in later years. Manufacturing, construction, and trade sectors experienced high average growth in 1980s. However, average growth in these sectors declined in recent years; and the finance sector stands out with strong growth performance since 2000s (Table 4.1). On the other hand, Bangladesh experienced a decade of stagnation in the 1980s, and the economy started flourishing since late 1990s. Bangladesh has been successful in continuing this success in re- cent years as well. Manufacturing, construction, trade sectors have been experiencing strong and steady growth since 1990s. Growth in finance sector has also been decent in recent years (Table 4.2).