How important is land as a factor of production in India? In a recent paper (The misallocation of land and other factors of production in India," Policy Research Working Paper Series 7221), we focused on its role, and examined how the misallocation of land has impacted the country’s manufacturing sector.
India provides a rich ground for examining fundamental questions like how important addressing land market distortions is for growth. Firms in India differ enormously compared to USA. The productivity of a US firm at the top decile is typically twice as high as that of a firm at the bottom decile in a typical manufacturing industry. This increases to five times more in India. This suggests considerable factor market misallocation within India. A similar narrative can be found in other countries in Africa and East Asia.
We computed misallocation indices for different factors of production (labor, land and buildings, other fixed assets) as well as for output for the manufacturing sector. This was computed at the sub-national district level. This approach enables us to assess the effects of ‘frictions’ on economic development, and in particular focus our attention on how the misallocation of land has impacted output per worker.
India’s spatial disparities in output per worker show considerable variation across districts. The spatial disparity in productivity is huge and differences in factor misallocation within India are larger than the differences found in many other countries. Districts with higher output per worker are largely concentrated in Gujarat, Rajasthan, Haryana, Punjab, and Maharashtra. Low output per worker is more prevalent in the eastern and poorer lagging regions of India (Bihar, UP, and Orissa).
While more productive enterprises manage to produce more than less efficient enterprises, the allocations of some factors of production are barely better than random. Indeed, there are many districts in India where factor allocation is worse than random. Two more subtle patterns also emerge. First, the misallocation of land and labour is worse than the misallocation of buildings (when it can be separated from land) and other fixed assets. Second, it is unfortunate that there appears to be a mild trend towards a worsening output and factor misallocation over time.
What was the impact of other factor market distortions? When we separate capital and employment, we estimate coefficients of roughly the same magnitude for both factors to explain the misallocation of output (or value added). When we consider land and buildings separately from other fixed assets, the coefficient on land and building is always much larger than that on other fixed assets. This is a striking result. In one simple regression, one standard deviation in land and buildings misallocation is associated with 0.62 standard deviation of value added misallocation. The corresponding figure for employment is only 0.40. Even though land and building account for a small fraction of final output and value added, they seem to play a disproportionate role in explaining the misallocation of final output. Misallocation of capital appears to account for very little of the misallocation of final output. Overall, this finding is consistent with the notion that land is the most distorted factor market and the least flexible factor of production. It is likely that huge land misallocation is also breeding the misallocation in other factors of production.
An important takeaway of our finding is that a standard deviation increase in the misallocation of all factors is associated with a 27% decrease in output per worker and most of this decline originates from the misallocation of land and buildings. While the gains from expanding supply, and particularly the supply of land are large, the gains from a better land allocation is even larger. When we correlate the misallocation of land and buildings at the district level with the misallocation of output and with the output per worker, land and buildings misallocation appears to be at the root of much of the misallocation of output and it accounts for a large share of the observed differences in output per worker. More precisely, across districts, a standard deviation in the misallocation of land and buildings accounts for about 20% difference in output per worker.
This line of work could be further extended to better understand what should be the priorities for policy action. Knowing about the within/between breakdown of misallocation is important since the policy tools to target both types of misallocation are likely to differ. For instance, further integration of financial markets within India or a reduction in the cost of migration between areas will likely affect misallocation (of capital and labour) across places first and foremost. Other policies such as a relaxation of the strict zoning policies in some cities, on the other hand, are expected to improve the efficiency of factor allocation in those areas where these regulations are the most binding. There is also a need to examine how factor market distortions are interacting with other emerging trends in urbanization, transport, and technology, given that the large manufacturing firms are leaving the mega cities and moving into intermediate size cities in search of lower land costs. While these relocations may be rational and justified from the perspective of the firm, they may worsen the allocation of factors in the country.
Alternatively, the worsening of factor allocation within district may be offset by an improvement of factor allocation across districts and states. Furthermore, the within/between decomposition of misallocation could be applied to the formal and informal sectors and how these are being impacted in the tradable and non-tradable sectors, and the gender component of the enterprises. While one expects some misallocation among male-owned and among female-owned enterprises, assessing how much aggregate misallocation is accounted for by female-owned enterprises not having access to factor inputs is of first-order importance to assess the economic consequences of gender discrimination in India. Finally, firms face constraints on their external finance that depend on the amount of land and building that they own, and so tracking how financial constraints and what firms own in terms of real assets affect how firms operate and produce is also a first-order question.
Emerging Lessons
So what are some of the emerging lessons on land misallocation and growth? Policy makers should continue to give importance to factor accumulation, but improved factor allocation, particularly land, can be an even bigger driver of growth. The misallocation of factor inputs hampers firm performance, and land and building distortions appear to be especially important. A one standard deviation decrease in the misallocation of land and buildings can bring about a 25% increase in output per worker in India. This is equivalent to a six-fold increase in the land supply for manufacturing establishments in these districts. More attention should also be given towards reducing huge spatial variation in allocation efficiency across districts and states. This spatial variation within India is of the same magnitude as the variation in allocation efficiency across countries of the world. Even though land and building account for a small fraction of final output and value added, they play a disproportionate role in explaining the misallocation of final output. Land is the least flexible factor of production and its misallocation also likely breeds the misallocations of other factors and output.
India provides a rich ground for examining fundamental questions like how important addressing land market distortions is for growth. Firms in India differ enormously compared to USA. The productivity of a US firm at the top decile is typically twice as high as that of a firm at the bottom decile in a typical manufacturing industry. This increases to five times more in India. This suggests considerable factor market misallocation within India. A similar narrative can be found in other countries in Africa and East Asia.
We computed misallocation indices for different factors of production (labor, land and buildings, other fixed assets) as well as for output for the manufacturing sector. This was computed at the sub-national district level. This approach enables us to assess the effects of ‘frictions’ on economic development, and in particular focus our attention on how the misallocation of land has impacted output per worker.
India’s spatial disparities in output per worker show considerable variation across districts. The spatial disparity in productivity is huge and differences in factor misallocation within India are larger than the differences found in many other countries. Districts with higher output per worker are largely concentrated in Gujarat, Rajasthan, Haryana, Punjab, and Maharashtra. Low output per worker is more prevalent in the eastern and poorer lagging regions of India (Bihar, UP, and Orissa).
While more productive enterprises manage to produce more than less efficient enterprises, the allocations of some factors of production are barely better than random. Indeed, there are many districts in India where factor allocation is worse than random. Two more subtle patterns also emerge. First, the misallocation of land and labour is worse than the misallocation of buildings (when it can be separated from land) and other fixed assets. Second, it is unfortunate that there appears to be a mild trend towards a worsening output and factor misallocation over time.
What was the impact of other factor market distortions? When we separate capital and employment, we estimate coefficients of roughly the same magnitude for both factors to explain the misallocation of output (or value added). When we consider land and buildings separately from other fixed assets, the coefficient on land and building is always much larger than that on other fixed assets. This is a striking result. In one simple regression, one standard deviation in land and buildings misallocation is associated with 0.62 standard deviation of value added misallocation. The corresponding figure for employment is only 0.40. Even though land and building account for a small fraction of final output and value added, they seem to play a disproportionate role in explaining the misallocation of final output. Misallocation of capital appears to account for very little of the misallocation of final output. Overall, this finding is consistent with the notion that land is the most distorted factor market and the least flexible factor of production. It is likely that huge land misallocation is also breeding the misallocation in other factors of production.
An important takeaway of our finding is that a standard deviation increase in the misallocation of all factors is associated with a 27% decrease in output per worker and most of this decline originates from the misallocation of land and buildings. While the gains from expanding supply, and particularly the supply of land are large, the gains from a better land allocation is even larger. When we correlate the misallocation of land and buildings at the district level with the misallocation of output and with the output per worker, land and buildings misallocation appears to be at the root of much of the misallocation of output and it accounts for a large share of the observed differences in output per worker. More precisely, across districts, a standard deviation in the misallocation of land and buildings accounts for about 20% difference in output per worker.
This line of work could be further extended to better understand what should be the priorities for policy action. Knowing about the within/between breakdown of misallocation is important since the policy tools to target both types of misallocation are likely to differ. For instance, further integration of financial markets within India or a reduction in the cost of migration between areas will likely affect misallocation (of capital and labour) across places first and foremost. Other policies such as a relaxation of the strict zoning policies in some cities, on the other hand, are expected to improve the efficiency of factor allocation in those areas where these regulations are the most binding. There is also a need to examine how factor market distortions are interacting with other emerging trends in urbanization, transport, and technology, given that the large manufacturing firms are leaving the mega cities and moving into intermediate size cities in search of lower land costs. While these relocations may be rational and justified from the perspective of the firm, they may worsen the allocation of factors in the country.
Alternatively, the worsening of factor allocation within district may be offset by an improvement of factor allocation across districts and states. Furthermore, the within/between decomposition of misallocation could be applied to the formal and informal sectors and how these are being impacted in the tradable and non-tradable sectors, and the gender component of the enterprises. While one expects some misallocation among male-owned and among female-owned enterprises, assessing how much aggregate misallocation is accounted for by female-owned enterprises not having access to factor inputs is of first-order importance to assess the economic consequences of gender discrimination in India. Finally, firms face constraints on their external finance that depend on the amount of land and building that they own, and so tracking how financial constraints and what firms own in terms of real assets affect how firms operate and produce is also a first-order question.
Emerging Lessons
So what are some of the emerging lessons on land misallocation and growth? Policy makers should continue to give importance to factor accumulation, but improved factor allocation, particularly land, can be an even bigger driver of growth. The misallocation of factor inputs hampers firm performance, and land and building distortions appear to be especially important. A one standard deviation decrease in the misallocation of land and buildings can bring about a 25% increase in output per worker in India. This is equivalent to a six-fold increase in the land supply for manufacturing establishments in these districts. More attention should also be given towards reducing huge spatial variation in allocation efficiency across districts and states. This spatial variation within India is of the same magnitude as the variation in allocation efficiency across countries of the world. Even though land and building account for a small fraction of final output and value added, they play a disproportionate role in explaining the misallocation of final output. Land is the least flexible factor of production and its misallocation also likely breeds the misallocations of other factors and output.
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