Agricultural Productivity Growth and the Role of Capital in South Asia (1980–2013)
1.1. Agricultural Growth and TFP: A Major Policy Objectives of the South Asian Countries
1.2. The Green Revolution Technology and Agricultural Growth in South Asia
1.3. Total Factor Productivity and Agricultural Sustainability
1.4. Agricultural Productivity Growth Analysis for South Asia
2. Materials and Methods
2.1. The study Countries
2.2. Analytical Framework: TFP Measurement
2.3. Estimation Using DEA
2.4. Determinants of TFP Changes
2.4.1. Theoretical Framework
2.4.2. Variables Explaining TFP Changes
2.4.3. Econometric Issues
3.1. Summary Characteristics of the Study Regions
3.2. Agricultural Productivity Growth and Associated Efficiency Changes
3.3. Determinants of TFP Changes
5. Conclusions and Policy Implications
Conflicts of Interest
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|Year||Value US$, 2005 Prices||Agriculture’s Share of Total Outlays|
|Annual Compound Growth Rate||0.170 ***||0.126 ***||0.142||0.155 ***||0.108 ***||0.050 **||0.082||0.084 ***|
|Crop output||Includes all seasons and varieties of cereals, roots and tubers, pulses, oilseeds, vegetables, fruits and cash crops for all the four countries. Cereals, roots and tubers, and pulses are measured in physical quantity (i.e., metric tons). For the other three outputs gross production value (constant 2004–2006 1000 I$) are calculated. We have used six output variables namely: (i) cereals (including rice, wheat, barley, maize, millet, sorghum, etc.); (ii) roots and tubers (includes Potatoes, sweet potatoes, cassava, etc.); pulses (all types, e.g., broad and horse beans, different types of peas and beans, lentils, etc.); cash crops (includes coffee, tea, tobacco, rubber, etc.); oilseed (all types, e.g., almonds, soybeans, coconuts, groundnuts, rapeseed, sunflower seed, linseed, cashew nuts, sesame seed, mustard seed etc.); and vegetable (all types, e.g., cabbages and other brassicas, tomatoes, lettuce and chicory, pumpkins, squash, cauliflowers and broccoli, gourds, eggplants, cucumbers and gherkins, green beans, carrots and turnips, okra, etc.) and fruits (apples, bananas, oranges, lemons and limes, grapefruit, citrus fruit, pears, cherries, apricots, plums and sloes, and peaches and nectarines, etc.).|
|Animal power||Number of total live draft animals (i.e., cattle and buffaloes)|
|Labour||Total economically active population (000) working in agriculture|
|Land area||Land area is measured as gross cropped area derived by multiplying arable land (000 ha) with cropping intensity (CI). The data for arable land and cropping intensity were taken from faostat and respective country’s national statistics, respectively. In Bangladesh, CI data was missing for the years 1980, 2012 and 2013. For India and Pakistan, CI data was available for the years 1990–2011. In Nepal CI information were available only for 1992, 2002 and 2012. Standard linear trend interpolation method was applied for these missing information. Similarly, for all the four countries, arable land data for the year 2013 is predicted.|
|Fertilizer||Consumption of fertilizer in terms of total nutrients (metric tons) is estimated. Total nutrients include nitrogen (N), potassium (K) and phosphorus (P) obtained from all types of fertilizers (e.g., urea, single superphosphate, triple superphosphate, diammonium phosphate, muriate of potash, etc.). For India, data for N, P and K consumption are taken from Agricultural Statistics at a Glance 2010 and 2013. Fertilizer consumption data (in physical quantity) for Bangladesh, are taken from the Year Book of Agricultural Statistics, 1983–1984, 1996, 2008, 2013 (BBS, various issues), and then converted into actual nutrient ingredients. For few years after 2006, some missing figures were replaced from Bangladesh Economic Review 2014. For Pakistan and Nepal, nutrient consumption figures for the years 2002–2012 were available in faostat. For the earlier years and 2013, a simple linear trend method is applied.|
|Irrigation||The proportion of land under irrigation is estimated as the ratio of total area equipped for irrigation (000 ha) and gross cropped area (GCA). Data for the earlier variable is taken from faostat. For all the country 2013 information was missing and is filled by interpolation method. GCA is the product of arable land (000 ha) and cropping intensity (CI). CI data for Bangladesh was collected from various issues of Year Book of Agricultural Statistics, 1983–1984, 1996, 2008, 2013 (BBS, various issues), whereas for Nepal data at only three points of time (1991/1992, 2001/2002 and 2011/2012) were available at the Pocket Book of Nepal, 2014 (CBS, 2014). For India information about CI since 2000/01 is readily available at the Statistical Year Book of India 2016, whereas for earlier years CI was calculated using information available at the Handbook of Statistics on Indian Economy 2015–2016. Land utilization statistics (total cropped area and net cropped area) for Pakistan since 1989/1990 were collected from Ministry of Food, Agriculture and Livestock, Pakistan. The missing information were filled by interpolation or extrapolation through simple linear trend method.|
|Variables||Description of variables|
|Technology capital||Using the information available in the faostat, this variable was constructed by adding the ratio of total number of agricultural researchers (FTE) and gross cropped area with the agriculture research spending as share of value added (agriculture, forestry and fishing). Simple linear inter and extrapolation methods were employed to obtain data for the missing years.|
|Mechanization level||This variable was constructed by adding three indicators:|
(a) Number of tractors available per GCA: The tractor numbers were collected from faostat. For Bangladesh and Pakistan, data were available till 2006; whereas for India and Nepal data till 2003 and 2008 were available respectively. The missing data points were filled through simple linear trend method.
(b) Agriculture and forestry energy use as a percentage of total energy use: The data for the period for 1980-2009 were taken from faostat, whereas through linear trend method values for the later years were predicted.
(c) Proportion of area equipped for irrigation is same as was used in estimating TFP and its components.
|Human capital||Average year of schooling for the population was taken from the Human Capital Report 2015 of the World Economic Forum .|
|Financial capital||Development flows to agriculture from all donors (disbursement in USD 2014 prices) with the share of credit to agriculture (including forestry and fishing) and total credit. Another component added here is the share of agricultural GDP spend for agricultural science and technology. The data was taken from faostat, which are available for different time periods for different countries. Interpolation method was applied to fill the missing data.|
|Natural capital||The variable was constructed as the ratio of arable land (ha) and total population. The data was taken from the faostat.|
|Herfindahl index of crop diversification||Herfindahl index of crop diversification (the value of the index is from 0 to 1 and higher value represents specialization) is estimated through using information about land under different crops available at faostat.|
|Cereals (metric tons)||34,232,352||214,834,964||26,185,959||6,260,500|
|Roots and Tubers (metric tons)||3,482,545||29,450,091||1,866,224||1,302,632|
|Pulses (metric tons)||423,922||13,507,441||920,964||202,630|
|Cash crops (gross production value, constant 2004–2006 1000 USD)||934,006||19,843,558||4,452,873||174,765|
|Vegetables and fruits (gross production value, constant 2004–2006 1000 USD)||1,076,900||30,428,075||2,465,146||598,418|
|Oilseed (gross production value, constant 2004–2006 1000 USD)||133,270||8,526,668||323,120||84,532|
|Cattle and buffaloes stocks (Head)||23,627,865||288,658,258||43,794,114||10,343,467|
|Total economically active population in agriculture (1000)||31,109||227,204||18,293||7851|
|Gross cropped area||1,456,162||22,122,079||2,868,725||411,961|
|Fertilizer (Total Nutrient)||1,111,026||15,088,612||2,922,503||24,847|
|Determinants of TFP change|
|Herfindahl index of crop diversification||0.68||0.38||0.39||0.58|
|Countries||TFP Level||Maximum TFP Level||TFP Efficiency Level||Technical Efficiency Level||Scale Efficiency Level||Mix Efficiency Level||Residual Scale Efficiency Level||Residual Mix Efficiency Level|
|Country||Period||TFP Change||Maximum TFP Change||TFP Efficiency Change||Technical Efficiency Change||Scale Efficiency Change||Mix-Efficiency Change||Residual Scale-Efficiency Change||Residual Mix-Efficiency Change|
|Lagged change in TFP (t-1 year)||0.7072 ***||0.0651|
|Technology capital||0.3775 **||0.1846|
|Mechanization level||−0.0019 ns||0.0055|
|Human capital||0.0104 *||0.0058|
|Financial capital||−0.2887 **||0.0918|
|Natural capital||1.0522 **||0.4364|
|Herfindahl index of crop diversification||0.1720 **||0.0870|
|Sargan test of overid. Restrictions||117.74 ns|
|Arellano-Bond test for AR(1) in first differences (z-statistic)||−5.68 ***|
|Arellano-Bond test for AR(2) in first differences (z-statistic)||0.39 ns|
|Difference-in-Sargan’s tests of exogeneity of instrument subsets:|
|GMM instruments for levels (null: H = exogenous) (χ2 4 df)||0.25 ns|
|IV instruments (null: H = exogenous) (χ2 2 df)||−0.00 ns|
|Number of observations||132|
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Anik, A.R.; Rahman, S.; Sarker, J.R. Agricultural Productivity Growth and the Role of Capital in South Asia (1980–2013). Sustainability 2017, 9, 470. https://doi.org/10.3390/su9030470
Anik AR, Rahman S, Sarker JR. Agricultural Productivity Growth and the Role of Capital in South Asia (1980–2013). Sustainability. 2017; 9(3):470. https://doi.org/10.3390/su9030470Chicago/Turabian Style
Anik, Asif Reza, Sanzidur Rahman, and Jaba Rani Sarker. 2017. "Agricultural Productivity Growth and the Role of Capital in South Asia (1980–2013)" Sustainability 9, no. 3: 470. https://doi.org/10.3390/su9030470