Labor Mobility, Market Integration, and Wage Convergence in Late 19th Century India

Explorations in Economic History 36, 246–277 (1999) Article ID exeh.1999.0718, available online at http://www.idealibrary.com on

Labor Mobility, Market Integration, and Wage Convergence in Late 19th Century India William J. Collins* Department of Economics, Vanderbilt University, Nashville, Tennessee 37235 I use district-level wage and food price data for the period 1873 to 1906 to investigate the impact of improvements in transportation on the price of labor in late 19th century India. Falling transport costs could have promoted regional wage convergence by facilitating both labor mobility and interregional commodity trade. There is, however, only qualified evidence of wage convergence in late 19th century India, and it appears that the steady-state dispersion of wages was not compressed by the forces of ‘‘globalization.’’ Relatively low rates of internal migration, weak Heckscher–Ohlin factor price convergence forces, and high climatic variability all contribute to this result. r 1999 Academic Press

Among the most prominent motifs in the tales of international trade theory is the dramatic ‘‘opening’’ of economic relations between two previously autarkic regions: it is the trade theorist’s opening curtain. Bertil Ohlin features it in Part I, Chapter I of his classic The Theory of Trade: ‘‘We start with two regions that have been isolated from each other and explore the circumstances under which trade will develop when relations between them are opened up.’’1 Even barring factor mobility, with a sufficient number of familiar underlying assumptions, the regions emerge from an autarkic gloom into the potentially Pareto-improved era of interregional trade and factor price equalization. The storyline is exciting, and it makes for great theater (and theory), but its empirical relevance remains underexplored.2 The 19th century’s extraordinary improvements in transportation and communication provide outstanding opportunities to observe the forces of international and interregional market integration. In colonial India the natural barriers of geo* I am grateful to Andre´s Antonius Gonza´lez, Claudia Goldin, Carol Heim, Jonathan Morduch, Sanjay Reddy, Joshua Rosenbloom, Jeffrey Williamson, the participants in the Third World Congress of Cliometrics (Munich 1997), and the participants in Harvard’s Economic History Lunch for insightful suggestions. I am also grateful for the support of a Graduate Fellowship from the National Science Foundation and a grant from the Chiles Foundation. All errors are my own. 1 Flam and Flanders, Heckscher–Ohlin, p. 86. 2 See O’Rourke and Williamson, ‘‘Late Nineteenth-Century’’ and ‘‘Open Economy Forces’’ for applications of trade theory to historical examples of factor price equalization. 246 0014-4983/99 $30.00 Copyright r 1999 by Academic Press All rights of reproduction in any form reserved.

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graphic distance were eroded throughout the second half of the century by the opening of the Suez Canal, the development of steamships, the establishment of a telegraph network, and the construction of an impressive railway system. In contrast to the European continent, the British governing India did not use tariffs to mute the impact of these globalization forces before World War I. This transport revolution had the expected effect on commodity prices both between India and Britain and within the Indian subcontinent—price gaps declined.3 However, the potential impact of falling transportation costs and increasing commodity market integration on Indian labor markets, and in particular on the price of labor across regions, still awaits investigation. Both factor mobility and trade-induced factor-price-equalization forces (as in the Heckscher–Ohlin trade model) could have promoted regional real wage convergence within India. Over the course of the 19th century, these forces could have gained momentum as the cost of transporting factors and goods between regions fell, but also could have been offset by relative price changes that favored some specialized regions over others.4 Furthermore, agricultural economies are vulnerable to climatic shocks that may disrupt any income convergence process, and it is clear that improvements in commodity market integration did not prevent the frequent occurrence of famine conditions in late 19th century India, even when they did mitigate mortality.5 Finally, India’s uniquely heterogeneous population and culture, in combination with the predominance of village-based agriculture, may have precluded much convergence via large-scale, long-distance factor mobility. The tension between the forces of regional convergence and divergence will be explored empirically through the following series of questions. Did the price of labor converge across regions as railroads opened the interior of the country, or did externally driven price changes and internally generated supply shocks offset relatively weak convergence forces? If there was convergence, was it driven by factor migration or by interregional trade in the spirit of Eli Heckscher and Bertil Ohlin? If not, what characteristics of the economy precluded regional convergence? Finally, what happened to the ratio of skilled/unskilled wages over time, and how did trends in the skill premium differ across regions? A wealth of regional wage data was collected by the colonial government in the late 19th century, and these data have been used in numerous regional studies and in constructing national average wage indices, but they have not been used to 3 Latham and Neal argue that ‘‘it was in India that the wheat world and the rice world met to form a single international market’’ before the first world war (‘‘International Market,’’ p. 273). Within British India, Hurd documents a decline in the coefficient of variation for wheat and rice prices within India from about 40% to under 20% from 1861 to 1910 and argues that this convergence is attributable to expansion of railways (‘‘Railways and the Expansion of Markets,’’ p. 270). 4 If all regions have diversified production, then according to Heckscher–Ohlin trade theory, relative commodity price changes should affect factor prices in the same manner everywhere. If, however, some regions are ‘‘outside the cone of diversification,’’ then relative price changes can affect regions differently. 5 McAlpin, ‘‘Dearth.’’

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characterize the nature, extent, or evolution of labor markets across the subcontinent.6 Thus, the paper has two goals: to document regional differences in real wage levels and trends in the late 19th century, and to explore the continuous interaction of integrating and disintegrating forces in a poor, open, agricultural economy. The findings should have implications for discussions of India’s standard of living, distribution of income, and general economic development (or lack thereof). More generally, they should provide insight into the processes of convergence and market integration as well as the strength (or weakness) of Heckscher–Ohlin links between commodity market integration and factor price convergence in a developing economy. Ultimately, only qualified evidence of real wage convergence emerges. The overall dispersion of real wages for three different groups of workers did not decline over the 1873 to 1906 period, though there is some narrowing of the dispersion in the areas near the cities of Bombay and Calcutta. Furthermore, while it appears that districts that started the period with relatively low real wages had faster wage growth than initially high-wage places, the estimated magnitude of this convergence force is not large, and there is no evidence that it became stronger over time. In this context, India’s monsoon-driven climatic shocks offset underlying convergence forces in the maintenance of a wide dispersion of wages across regions. MECHANISMS OF INTEGRATION Parts of the Indian subcontinent, especially coastal regions, had been engaged in international and interregional trade for centuries before 1800.7 D. R. Gadgil, however, asserts that ‘‘In the first half of the nineteenth century, the state of internal communication in India was extremely defective. In most parts of the country roads as such did not exist. . . . The Indus and the Ganges, with their tributaries, were the only river systems that were navigable to any large extent.’’8 In short, the Indian village, home to about 90% of the country’s population, was ‘‘an almost entirely self-sufficient unit.’’9 Although economic historians have recently emphasized India’s long history of external commercial contact, David Washbrook recognizes that ‘‘developments from the second half of the nineteenth century greatly strengthened those influences.’’10 By the end of the 19th century, the relative isolation of Indian villages had been, to a large extent, undermined by increased economic contact with other Indian regions and with the rest of the 6 See Krishnamurty, ‘‘Real Wages,’’ Reddy, ‘‘Official Data,’’ and Kumar, ‘‘Agricultural Wages,’’ for examples of regional studies. See Kuczynski, ‘‘Condition,’’ and Mukherjee, National Income, for examples of national wage indices. See Bhattacharya, ‘‘Trend of Wages,’’ for an early effort to characterize the overall dispersion of wages, though at a higher level of aggregation than is done in this paper and without region-specific cost of living deflators. 7 See Subrahmanyam, Political Economy. 8 Gadgil, Industrial Evolution, p. 3. 9 Gadgil, p. 9. 10 Washbrook, ‘‘Commercialization,’’ p. 129.

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world. For example, the real value of international trade nearly tripled between 1870 and 1910 compared to a rise in national income of about 42% and net ton–miles of goods transported by railroads rose from 2,450 million in 1882 to 11,772 million in 1910.11 The establishment of British governance over the greater part of the subcontinent may have been by itself an important integrating force following the decline of the Moghul Empire. Morris D. Morris claims that the volatile course of Indian history interfered with both commerce and capital accumulation.12 Political instability, moreover, is a cornerstone to Deepak Lal’s provocative theory of the development and persistence of autarkic villages in a ‘‘Hindu equilibrium.’’13 Aside from the 1857 revolt (after which the Crown took over direct administration of India), the British succeeded in maintaining order which in turn established a large economic space largely free from artificial internal barriers to commerce and factor mobility.14 Discussions of India’s strengthening economic ties to the outside world often cite the U.S. Civil War as ‘‘the first event, in the western world, to act on India suddenly and to have a very important economic effect.’’15 Aside from the gains of cotton cultivators during this period, the real significance of this event is that it ‘‘clearly and dramatically revealed a break in the economic isolation of India.’’16 This break was widened when on November 17, 1869 the Suez Canal, which had been ‘‘cut by French energy and Egyptian money for British advantage,’’ was inaugurated.17 The canal shortened the distance from Britain to Bombay from 10,700 to 6,200 miles, and to Calcutta from 11,900 to 8,000 miles.18 By 1883–1884, the canal route’s superiority translated into approximately 85% of the total value of trade between Britain and India moving via the canal.19 The canal was particularly important to establishing a link between Indian and European wheat markets as wheat often became weevil-infested during the long voyage via the Cape.20 Moreover, the canal’s opening spurred steamship building and innovation, which in turn lowered transport costs still further. The implementation of new telegraph technology also contributed to the extension of Indian commodity markets. The establishment of a telegraph network linking India’s major towns with one another in the 1850s and then 11 The real value of trade is from Bhatia (1969). Real income per capita is multiplied by the population to get total NNP. These figures are from Heston (1983, pp. 402, 410). Railway data are from Morris and Dudley, ‘‘Railway Statistics,’’ pp. 216–217. 12 ‘‘Towards a Reinterpretation,’’ pp. 608–609. 13 Hindu, pp. 31–74. 14 As Moghul control disintegrated after 1707, local transit tolls compounded the deleterious effects of political fractiousness (Maddison, ‘‘Origins,’’ p. 12). 15 Gadgil, Industrial Evolution, p. 14. 16 Gadgil, p. 18. 17 Fletcher, ‘‘Suez Canal,’’ p. 564. 18 Latham, International Economy, p. 27. 19 Fletcher, ‘‘Suez Canal,’’ p. 568. 20 Latham, International Economy, p. 75.

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linking India with Europe and other parts of Asia in the 1870s ensured that information flowed easily from region to region. In 1870–1871, 66,000 private foreign and 472,000 private inland telegrams were sent, and by 1900–1901, this volume had increased by a factor greater than 10.21 Commodity price ratios (Britain/India) fell substantially from 1873 to 1913 as a result of these transportation and communication improvements: cotton from 1.57 to 1.20, rice from 1.93 to 1.26, linseed from 1.35 to 1.22, and so on.22 By 1870 India had emerged as a large exporter of primary products (cotton, opium, rice, oil seeds, jute, and wheat) and a large importer of manufactured goods (cotton textiles and yarn, iron and steel, brass and copper, railway equipment).23 Over the next 40 years India’s terms of trade improved as the price of agricultural goods rose relative to that of manufactures by about 30%.24 The impact of these relative price changes could have differed significantly across regions according to their production specialization. For example, the price of jute rose relative to both import and other export prices from 1873 to 1910, and since nearly all of India’s jute was produced in Bengal, it seems likely that the region benefited from the relative price rise. On the other hand, over the same period, the absolute price of cotton did not rise in India, and therefore cotton-producing areas of Bombay and Berar might have lost ground relative to other regions. The empirical work offered below will investigate whether or not relative price changes had a detectable impact on regional real wage trends. Such price changes could promote either regional wage convergence or divergence in the late 19th century depending on whether or not initially low wage areas benefitted more than other areas from the relative price shifts. Whereas the Suez Canal and the steamship established a higher level of integration between India, the rest of Asia, and Europe, the story of India’s internal integration is dominated by the railroad—as Daniel Buchanan metaphorically put it, ‘‘The armour of the isolated, self-sufficient village was pierced by the steel rail.’’25 The first railroads were built in the 1850s, typically running out from one of the major port cities (Bombay, Calcutta, Madras) to the rural hinterlands and soon thereafter extending to the larger inland towns. In 1860, India had only 838 miles of railway open, but by 1870, 4,771 miles were open, and by 1900, 23,628 miles crossed the subcontinent.26 Overland freight rates per mile were approximately 80% lower using rail as opposed to cart transport, and this permitted profitable bulk shipments of grain from region to region.27 Both total 21

Buchanan, Development, p. 179. Prices and Wages, 1919 edition. 23 Latham, International Economy, pp. 74, 78. 24 McAlpin, ‘‘Price Movements,’’ p. 903. 25 Buchanan, Development, p. 189. 26 Hurd, ‘‘Railroads,’’ p. 267. Railroads were first developed by private interests with government guarantees and approval, but the government became a more active and direct developer and owner over time. See Kerr, Building, for an extensive study of India’s railways. 27 Ibid, p. 268. 22

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passenger–miles and net ton–miles of goods more than quadrupled between 1882 and 1910. Over the same period, the average rate charged per ton–mile fell from an index value of 100 to 61, whereas the average rate per passenger per mile fell only from 100 to 90.28 These declines stand in marked contrast to the movement of the overall price level, which rose by more than 50%.29 Hurd has demonstrated that the increasing internal mobility of goods associated with this transport price decline generated substantial commodity price convergence within India. LABOR MARKET STRUCTURE AND POTENTIAL BARRIERS TO INTEGRATION The Indian economy was undoubtedly transformed in many ways during the late 19th century, but Krishnamurty has shown that the basic sectoral distribution of Indian male workers remained quite stable: in 1881 approximately 72.4% of the male workforce fell into the Census’s agricultural category, which almost matches the 1911 figure of 74.5%.30 Clearly, agriculture accounted for the lion’s share of Indian economic activity, and this was probably just as true at the end of the 19th century as it was at the beginning. Only about 10% of the labor force were employed in manufacturing activities between 1881 and 1951, and another 8 or 9% worked in service. Nearly all workers in agriculture were classified as either ‘‘ordinary cultivators’’ (54% of the male labor force and 41% of the female labor force in 1911) whose income derived from their labor in combination with either owned or tenanted land, or as ‘‘agricultural laborers’’ (15% of men and 33% of women in 1911) whose income derived primarily from their labor.31 Many of the less substantial cultivators supplemented their income by working occasionally for wages, and many laborers had access to their own small plots of land.32 In such an environment, could geographic labor mobility have promoted regional real wage convergence? Internal migration appears to have been small relative to the size of the population, despite sizable differences in regional real wages which are documented below.33 In 1881, no less than 97% of the population 28

Morris and Dudley, ‘‘Railway Statistics.’’ Mukherjee, National income, p. 94. 30 Krishnamurty, ‘‘Occupational Structure,’’ p. 533. 31 Ibid, p. 535. The enumerated female workforce was about half the size of the male workforce (Statistical abstract relating to British India 1915, table 19). 32 The array of employer–laborer relationships in agriculture was far too complex to be reviewed in detail here, but a fundamental distinction between ‘‘free’’ and ‘‘attached’’ agricultural labor should be recognized. Attached laborers were bonded in one way or another (tradition, debt, heredity) to a particular employer and were required to provide agricultural and/or household services to that employer in exchange for subsistence requirements and some perquisites. Free laborers contracted for a specific task or length of time, usually at a higher daily rate than an attached laborer though generally without claim to the same bundle of perquisites as attached labor. See Kumar for a discussion of free and attached labor in 19th century Madras (‘‘Agricultural Wages’’ or Land and Caste). See Thorner and Thorner for a very general characterization of labor arrangements across India in the mid 20th century (Land and Labour in India). 33 Davis, Population; Zachariah, Internal Migration. 29

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had been born in the province of current residence, which prompted the 1881 Census to claim that ‘‘the Indian population is in no instance a people that desires to leave its home, and so long as it can obtain a fair amount of subsistence in its own village lands it never migrates.’’34 Thirty years later, the Census found that 91.3% of the population were born in the district of residence which elicited the remark that ‘‘The first thing which strikes one in connection with migration is its comparatively small volume.’’35 The majority of those living outside their birth-district were residing in an adjacent district, and ‘‘marriage migration’’ was one of the main contributory factors to this movement.36 Temporary or seasonal movements may not be reflected in this census-based characterization of migration, but it is clear that long-distance, permanent migration was rather uncommon in 19th century India.37 Even in the post-Independence period, Paul Cashin and Ratna Sahay remark on the lack of internal migration in India despite large regional income differentials.38 Kingsley Davis suggests a number of explanations for the relatively low migration rates within India including the caste system, early marriage, equal division of inheritance among sons, linguistic barriers, and very low levels of education.39 It is also likely that subsistence level incomes constrained mass internal migration, especially costly moves over long distances. Furthermore, using an option-theory approach to migration decisions, Michael Burda demonstrates that a high level of income uncertainty in a potential destination can suppress migration even for risk neutral workers in the presence of fixed moving costs.40 Empirically discriminating amongst the numerous constraints on labor mobility in colonial India lies beyond the scope of this paper, but it is clear that this web of social characteristics presented a formidable challenge to the establishment of anything resembling a national market for labor. Nonetheless, it would be mistaken to conclude that Indian workers were unresponsive to opportunities for material improvement. Indian migrants did respond to economic incentives, especially to land availability and high wages in 34 Census of British India 1881, Vol. I, p. 218. The Statistical Abstract Relating to British India (1894) reports that the provinces of Madras, Bombay (and Sind), Bengal, Punjab, and the United Provinces were all more than 100,000 square miles and less than 152,000 square miles in size, which makes them all somewhat larger than the United Kingdom (95,000) and about the size of Norway (125,000), according to the New York Times Atlas of the World, 1980. Within the British ruled portion of India, the average district was approximately 3,900 square miles (larger than Delaware and smaller than Massachusetts). 35 Census of India 1911, Vol. I, p. 91. 36 ‘‘Marriage migration’’ refers to the practice of selecting a bride from a neighboring village which incidentally might have been located in a different district. 37 Cross-country comparisons of internal migration rates are difficult due to the incomparability of geographic units and different incentives for and barriers to movement, but the Indian rate appears to be substantially lower than the United States’ rate (the usual comparison). See Eldridge and Thomas, Population Redistribution, for the United States and Zachariah, Internal Migration, for India. 38 Cashin and Sahay, ‘‘Internal Migration.’’ 39 Davis, The Population of India and Pakistan. 40 Burda, ‘‘Migration.’’ Also see O’Connell, ‘‘Migration Under Uncertainty.’’

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places like Burma and Assam and to industrial employment opportunities in Calcutta and Bombay.41 Furthermore, India’s external migration was large in absolute numbers (Davis estimates about 6 million on net from 1834 to 1937), though the migrants probably had a larger labor market impact on the receiving areas than on the areas of origin. These examples of mobility, however, were responses to new and quantitatively limited employment opportunities outside the traditional Indian economic structure. The picture of geographic mobility that emerges is one in which movement within the dominant, traditional economy was limited for all the reasons suggested above, but movement was evident in response to the new opportunities which emerged outside the traditional system and in response to seasonal factors as well. Taylor and Williamson have argued that large factor movements played a critical role in driving real wage convergence in the Atlantic economy between 1870 and 1910, but it seems unlikely that India’s relatively modest migration rates could have been sufficient to drive wage convergence as impressive as that observed between Europe and the New World.42 Even in the absence of factor mobility, however, it is well known that within the framework of a simple Heckscher–Ohlin model of international (or interregional) trade, the exchange of commodities can generate factor price convergence.43 The logic is straightforward, though the underlying assumptions are numerous: commodity trade establishes commodity price equalization which in turn leads to factor price equalization if different regions produce the same set of goods with the same technologies. Effectively, trade equalizes the demand for factors across regions, thereby replicating the equilibrium that would ensue if there were perfect factor mobility. If the extensive improvements in transportation networks did not encourage enough labor mobility to equalize wages across Indian regions, then perhaps commodity market integration served as an effective substitute. Essentially, late 19th century India provides an opportunity to identify the strength of Heckscher–Ohlin links in an economy with strong commodity price convergence, but rather low rates of factor mobility. REGIONAL WAGE DATA: SOURCES, RELIABILITY, AND USES Beginning in 1873 the Office of the Director-General of Commercial Intelligence began assembling, organizing, and publishing (as a supplement to the 41 See de Haan, ‘‘Migration in Eastern India,’’ and Das Gupta, ‘‘Factory Labor.’’ Das Gupta observes that migration to Bengal’s jute mills was initially (1890s) drawn from relatively nearby districts, but nonlocal labor (from 300 to 500 miles away) came to dominate employment in the mills in the early 20th century (1976, p. 299–300). Morris argues that ‘‘Indian labor will and does move long distances from rural agricultural occupations into urban industrial ones’’ (‘‘Labor Market,’’ p. 178). Clearly though, new industrial employment opportunities were quite few relative to the size of the agricultural labor force. H. S. Jevons noted in 1918 that ‘‘The peculiar and important feature of Indian industrial labor is that much the larger part of it is still attached to the soil, that is to say retains a close family and financial connection with the ancestral village’’ (‘‘Labor Question,’’ p. 197). 42 Taylor and Williamson, ‘‘Convergence.’’ 43 Samuelson, ‘‘International Trade.’’

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Gazette of India) retail food price quotations that had been collected by the various provinces previously (wholesale prices were added later). In the same year, wage quotations for male agricultural laborers, artisans, and horse-keepers (‘‘syce’’) were collected at the district level for the first time. In 1878, a separate volume was printed covering prices for the 1861 to 1876 period, and beginning in 1884 the Office published annually a volume entitled Prices and Wages in India (hereafter P&W).44 Hurd used these rice and wheat price data to make inferences about commodity market integration within India, but he did not make use of the publication’s vast quantity of wage data, which include average monthly wages of skilled and unskilled labor for a number of districts throughout India, as well as the pay of postal runners and postmen, canal workers, and some industrial workers (in paper mills, rice mills, mines, a brewery, and so on). The Government’s precise motives for having district officials collect the wage data are unclear, but it is likely that the effort grew from a more general official interest in standards of living and the logistics of famine relief. The widest geographic coverage is provided by the reports of average monthly wages (over the last six months of the year) for agricultural laborers, horsekeepers, and artisans for more than 60 districts (plus a number more in Burma) beginning in 1873 and running through 1906 for most districts in the sample.45 The problems inherent in such wage data are well known and are highlighted in the note preceding the wage section of P&W. Underemployment, payment in kind, unrepresentative geographic sampling (including town versus village rates), and differences in the way that labor was hired and remunerated across regions are difficult, if not impossible, to address in an entirely satisfactory manner. Moreover, P&W often reports the prevailing wage rates as a range without an indication of how workers were distributed across it (I use the range’s midpoint). Obviously, these data must be interpreted with care and caution. The imprecision of the data is a consequence of the difficulty of measuring wages, especially agricultural wages, in any developing country at any time; it is not a consequence of a lack of effort or expense to compile the statistics accurately. For example, the 1890 issue of P&W asserts that the volume [is] as complete an account of the course of prices, wholesale and retail, and wages in India, as it is possible to make such a record in present conditions . . . and the Government of India desires to impress upon District Officers the importance of careful verification of all such returns before they are transmitted to the Local Government. The Local Governments again should subject them to verification before they are published in the official Gazettes and sent to the Government of India.46

44 See Datta (Vol. I, Appendix B and G) for a description (and criticism) of how the price and wage statistics were collected in each province. 45 Entire provinces begin to drop from the sample in 1907, and so I only use the data up to 1906. The collection was eventually discontinued in favor of a quinquennial wage survey, first conducted in 1911. 46 Prices and Wages, 1890 issue, p. iv.

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Appendix 2 offers a number of checks on the basic accuracy of the data, and I make explicit comparisons with K. L. Datta’s work on wages in the latter part of this period, but as B. R. Tomlinson has remarked, ‘‘One econometric skill well-developed in all South Asianists is the ability to expose the fragility of data they wish to disbelieve’’ (1993, p. 2). The truly important issues of accuracy for the arguments made in this paper, especially those regarding wage convergence, concern potential sources of systematic bias in the data. For example, wage and price measurements could have become more (or less) accurate over time, or the commercialization and monetization of rural areas might have changed the structure of remuneration in a way not fully captured by the wage reporters (for example, cash versus in-kind wages). Moreover, in general, measurement error will tend to cause attenuation bias (that is, bias toward zero) in coefficient estimates of regressions that employ inaccurately measured explanatory variables.47 These cautions should be borne in mind throughout the analysis, and additional considerations will emerge as the investigation moves forward. Four different nominal wage series are extracted for use here, of which the first three will be referred to as the ‘‘main series.’’ First, the wages for agricultural laborers clearly represent the most important group numerically, but theirs was a difficult wage to measure accurately. Second, the wages of artisans are supposed to represent masons, carpenters and blacksmiths, which begs the question of whether or not it is reasonable to aggregate over these skilled groups. Third, the wage series for horse-keepers (intended to represent servants) is cited in P&W as the most likely of the three main series to be an accurate measure, albeit for a relatively small group of workers. Finally, the wages for postmen, though published in P&W, were collected separately from the three main series and should be quite accurate. This series pertains to a selective group of government workers, however, and it covers a more limited geographic scope than the three other wage series. The nominal wages reported in P&W are transformed into real wages by using district-level annual average retail price observations for rice, wheat, and gram.48 The three foods are given equal weight in the construction of district-specific cost-of-living series. Of course, this is a relatively limited ‘‘market basket’’ on which to base calculations of the real wage, but since the various food grains are to a large extent substitutes for one another, further expanding the basket along this dimension probably would not add much flavor to the measure. It would be most helpful to add textiles or other household items to the basket, but as far as I know systematic district-level price data for such items do not exist. Nonetheless, food grain consumption certainly did account for a large proportion of the average Indian worker’s budget, and therefore, the district level cost-of-living series

47

See Greene (Econometric Analysis, pp. 280–283) for a discussion of attenuation bias. Although rice prices are generally available for each district in each year of the sample, occasionally wheat or gram prices had to be estimated on the basis of price data available for ragi. Gram is a pulse like a chickpea. Ragi is a millet. The method of estimation is described in Appendix 3. 48

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WILLIAM J. COLLINS TABLE 1a Agricultural Labor Real Wage Index: East Bengal in 1870s ⫽ 100 (decade averages)

East Bengal (9) Bengal (9) United Prov. (11) Rajput. & C.I. (4) Punjab (6) Bombay (7) Sind (2) C.P. & Berar (4) Madras (7) Mysore & Coorg (3) Burma (2)

1873–1879

1880–1889

1890–1899

1900–1906

100 80 71 52 97 88 155 105 55 78 172

119 99 73 62 109 101 151 98 67 94 272

100 83 61 54 97 88 163 69 48 75 191

107 87 58 61 102 80 143 68 46 78 172

Note. District wages are weighted by the 1891 population to form provincial averages. The number of districts per province for which data are available is noted in parentheses. Following the geographic divisions in Prices and Wages in India, in Tables 1a through 1c East Bengal includes observations for the following districts: Rangpur, Dinajpur, Dacca, Backerganj, Chittagong, Sylhet, Cachar, Goalpara, and Lakhimpur. Bengal includes Bardwan, Midnapur, Calcutta (n.a. for agricultural labour), Murshidabad, Patna, Muzaffarpur, Mongyr, Purnea, Cuttack, and Hazaribagh. United Provinces include Muzaffarnagar, Saharanpur, Meerut, Bareli, Agra, Cawnpore, Allahabad, Mirzapur, Lucknow, Fyzabad, and Sultanpur. Rajputana and Central India include Jaipur, Ajmer, Indore, and Gwalior. Punjab includes Delhi, Ludiana, Amritsar, Rawalpindi, Multan, and Peshawar. Sind includes Karachi and Shikarpur. Bombay includes Bombay, Ahmedabad, Surat, Dhulia, Ahmadnagar, Belgaum, and Baroda. Central Provinces and Berar include Nagpur, Jubbulpore, Raipur, and Akola. Madras includes Ganjam, Vizagapatam, Bellary, Madras (n.a. for agricultural labour), Tanjore, Tinnevelly, Coimbatore, and Salem. Myore and Coorg include Bangalore, Mysore, and Coorg. Burma includes Rangoon and Moulmein. Data for Secunderbad (Hyderabad) are not included in Table 1, but do enter subsequent regression analysis. Source. Figures are calculated using data in Prices and Wages in India, various years.

should serve as fairly good proxies for price differences across regions and over time.49 See Appendix 4 for an alternative real wage calculation which uses different consumption baskets across regions. Decade-averaged real wage indices for each group of workers are reported by area in Tables 1a–1d. The overall real wage time trends fit the broad pattern of Indian economic history: a major famine from 1876–1878 was followed by the 49 In previous versions of this paper, the cost of living (COL) deflator was first constructed using only rice prices (the most widely available series), and then was constructed using both rice and wheat. This version incorporates gram into the COL deflator as well. The results are qualitatively similar in each case, except that convergence appears stronger when only rice is used. While acknowledging that workers in different regions consumed substantially different food grains, a common COL basket is adopted for making real wage comparisons, and this basket makes use of the most widely available price evidence. See Appendix 4 for more discussion. Sunanda Krishnamurty cites a Bombay village study by Harold Mann in 1917 that estimated that 87.5% of a laborer’s annual expenditure on food went towards grain (‘‘Real Wages,’’ p. 85).

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LABOR IN 19th CENTURY INDIA TABLE 1b Artisan Real Wage Index: East Bengal in 1870s ⫽ 100 (decade averages)

East Bengal (9) Bengal (10) United Prov. (11) Rajput. & C.I. (4) Punjab (6) Bombay (7) Sind (2) C.P. & Berar (4) Madras (8) Mysore & Coorg (3) Burma (2)

1873–1879

1880–1889

1890–1899

1900–1906

100 86 93 80 133 150 216 175 95 108 227

141 99 98 95 148 173 258 190 124 135 197

122 90 82 105 145 148 241 124 97 108 177

123 99 73 101 162 130 230 115 92 120 219

Note. The artisan category includes carpenters, masons and blacksmiths. Districts within each provincial category are listed in the notes to Table 1a. Source. Figures are calculated using data in Prices and Wages in India, various years.

relatively prosperous 1880s, which in turn was followed by the disastrous 1890s, which included two widespread famines (1896–1897, 1899–1900). In general, the United Provinces, Madras, and the combined Rajputana and Central India regions were the lowest real wage areas. East Bengal, Punjab, and Bombay (aside from agricultural labor wages) were relatively high wage areas, as were Burma and Sind (although these last two regions have relatively few observations on which to base the calculations). The Central Provinces and Berar were relatively highpaying areas in the 1870s and 1880s, but relatively low-paying areas in the 1890s and 1900s. Food prices rose much faster than nominal wages in this region in the

TABLE 1c Horsekeeper Real Wage Index: East Bengal in 1870s ⫽ 100 (decade averages)

East Bengal (9) Bengal (10) United Prov. (11) Rajput. & CI (4) Punjab (6) Bombay (7) Sind (2) CP & Berar (4) Madras (8) Mysore & Coorg (3) Burma (2)

1873–1879

1880–1889

1890–1899

1900–1906

100 91 92 75 113 110 148 166 88 77 153

125 105 97 87 123 128 162 147 102 95 173

108 88 79 76 102 112 154 104 80 84 154

111 91 74 71 108 107 169 87 77 99 160

Note. Districts within each provincial category are listed in the notes to Table 1a. Source. Figures are calculated using data in Prices and Wages in India, various years.

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WILLIAM J. COLLINS TABLE 1d Postmen Real Wage Index: Bengal in 1870s ⫽ 100 (decade averages)

Bengal (4) United Prov. (5) Rajput. & C.I. (2) Punjab (2) Bombay (6) Sind (2) C.P. (4) Madras (4)

1873–1879

1880–1889

1890–1899

1900–1906

100 107 — 77 79 78 115 71

107 93 95 89 87 90 111 74

90 73 88 83 77 87 87 60

87 78 81 85 73 95 84 62

Note. Bengal includes Calcutta, Dacca, Patna, and Cuttack. United Provinces include Lucknow, Fyzabad, Allahabad, Aligarh, and Gorakhpur. Rajputana and Central India include Ajmer and Indore. Punjab includes Lahore and Peshawar. Sind include Karachi and Shikarpur. Bombay includes Bombay, Ahmedabad, Dhulia, Ratnagiri, Poona, and Dharwar. Central Provinces include Nagpur, Jubbulpore, Raipur and Sambalpur. Madras includes Madras, Bangalore, Tinnevelly, Mangalore, Vizagapatam. Source. Figures are calculated using data in Prices and Wages in India, various years.

1890s, and Visaria and Visaria and the 1898 Famine Commission note that famine relief activities in this regions were ‘‘least adequate’’ in that decade.50 K. L. Datta’s Report on the Enquiry into the Rise of Prices in India is very critical of the nominal wage data reported in P&W. Therefore, I have made an effort to calculate a series of interregionally comparable real wages using Datta’s agricultural nominal wage data for the post-1890 period.51 In the early 1890s, Datta’s figures, like those in P&W, imply that real wages in Madras, the United Provinces, and the Central Provinces were relatively low compared to those in Bengal, Punjab, Assam, and Sind. Going beyond simple characterizations of ‘‘high’’ and ‘‘low’’ wage areas reveals some substantial differences between the P&W and Datta real wage series, however. For example, Datta’s figures suggest that real wages in Bombay were much lower relative to East Bengal than implied by P&W (by up to 57%). Moreover, Datta’s real wages trend upward on average, whereas those in P&W are stagnant at best. Therefore, the real wages estimates of Tables 1a–1d are reported with a note of caution, Appendix 2 explores these issues in more depth, and the convergence results will be checked, when possible, against similar tests using the Datta figures.

50 Visaria and Visaria, ‘‘Population,’’ p. 528. The Report of the Indian Famine Commission, 1898, evaluates the Central Provinces’ relief measures as follows: ‘‘While we recognize the great exertions of the Chief Commissioner and of the officers under him . . . we regret to have to express the opinion that the degree of success in the saving of life and relief of distress was not all that it should or might have been’’ (p. 175–176). 51 More details are discussed in Appendix 2.

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MEASURES OF CONVERGENCE AND INTEGRATION Hurd argues that commodity market integration improved within India, and Latham and Neal argue that commodity market integration improved between India and the rest of the world, but there has been no systematic use of factor price data to assess factor market integration.52 Some discussion of what is meant by ‘‘labor market integration’’ is required because the concept remains somewhat ambiguous in the historical literature for at least two reasons. First, the concept of market integration, whether applied to commodities or factors, is inherently relative. Hence George Stigler and Robert Sherwin assert that ‘‘no unique criterion exists’’ for determining the extent of a market, and Joshua Rosenbloom suggests that integration is ‘‘a matter of degree.’’53 Therefore the concept takes on empirical meaning only through comparisons either across places or over time. Second, different authors have emphasized different integrating mechanisms. Rosenbloom emphasizes geographic mobility in his notion of labor market integration: a shock-induced wage gap between two places in an integrated market should be eroded by migration from the low wage to the high wage area. On the other hand, Matthew Slaughter recognizes that trade in goods might substitute for factor mobility such that the integrated equilibrium can be replicated without factor movement (this draws on the familiar factor price equalization result from Heckscher–Ohlin trade theory): ‘‘If the measure of integration is defined as price equalization arising from any source, then FPC [trade-induced factor price convergence] seems to be as legitimate a cause of integration as migration is.’’54 Despite different emphases on the underlying causes of integration, both Rosenbloom and Slaughter use wage data to assess the evolution of labor market integration in the United States. Furthermore, both focus on the how the geographic dispersion of wages changed over time, rather than on whether wage movements in different regions were highly correlated. Such analyses of wage dispersion are actually more closely related to the macroeconomic convergence literature than to the commodity market integration literature. The fundamental difference between the approaches is that convergence studies emphasize changes in the geographic distribution of wages (or incomes or prices) over time, whereas market integration studies focus on the degree of correlation between time series for different locations.55 This paper primarily employs the convergence study methodology in its exploration of Indian wages because it is concerned with the long-run 52 Hurd, ‘‘Railways and the Expansion of Markets’’; Latham and Neal, ‘‘International Market.’’ See Cha, ‘‘Integration and Segmentation,’’ for a revisionist view of world rice markets. 53 Stigler and Sherwin, ‘‘Extent of the Market,’’ p. 562; Rosenbloom, ‘‘Was There a National Labor Market,’’ p. 628. 54 Slaughter, ‘‘The Antebellum Transportation Revolution,’’ p. 4. 55 In determining the ‘‘extent of the market’’ Stigler and Sherwin argue that, ‘‘If we find closely parallel price movements [at different locations], the loci of the prices are in the same market’’ (‘‘Extent of the Market,’’ p. 557). See Cha, ‘‘Integration and Segmentation,’’ and O’Connell and Wei, ‘‘The Bigger They Are,’’ for recent advances in the study of commodity market integration.

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WILLIAM J. COLLINS TABLE 2 Coefficients of Variation in Real Wages across Indian Districts (Excluding Burma)

Agricultural labourer National (63) East (18) North (19) Central (7) West (9) South (10) Artisan National (66) East (19) North (19) Central (8) West (9) South (11) Horsekeeper National (66) East (19) North (19) Central (8) West (9) South (11) Postman National (29)

1873–1879

1880–1889

1890–1899

1900–1906

0.337 0.253 0.275 0.306 0.318 0.279

0.332 0.244 0.320 0.210 0.290 0.313

0.369 0.244 0.327 0.207 0.363 0.311

0.355 0.236 0.332 0.267 0.320 0.294

0.367 0.249 0.293 0.315 0.312 0.203

0.383 0.331 0.343 0.287 0.320 0.228

0.391 0.386 0.371 0.209 0.306 0.236

0.367 0.274 0.466 0.178 0.320 0.243

0.277 0.160 0.192 0.394 0.204 0.225

0.246 0.188 0.200 0.246 0.225 0.193

0.246 0.206 0.182 0.178 0.224 0.164

0.250 0.164 0.239 0.177 0.256 0.174

0.298

0.259

0.213

0.198

Note. The coefficient of variation in a particular year is the standard deviation of wages divided by the mean. The maximum number of districts within each region’s sample is in parentheses (occasionally the actual number is less due to missing observations). Due to data limitations, the postman series can only be calculated for 1870 and 1875, 1880 and 1885, 1890–1899, and 1900–1906. Source. The figures are calculated using the real wage estimates derived from Prices and Wages in India, various issues.

evolution of labor markets as revealed in long-run movements of wages rather than the degree of correlation in short-run wage movements. Following the convention of Robert Barro and Xavier Sala-i-Martin, let ‘‘␴-convergence’’ refer to a downward time-trend in the cross-section dispersion of wages, and let ‘‘␤-convergence’’ refer to a tendency for initially low-wage areas to experience faster wage growth than high-wage areas.56 The coefficient of variation, also used by Hurd in his study of Indian commodity prices, provides a measure of the dispersion of real wages across India and can be used to identify ␴-convergence. Table 2 reports this figure for each decade for four occupations. There is no evidence of ␴-convergence at the national level from 1873 to 1906 for agricultural laborers, artisans, or horse-keepers, but the coefficient of variation does decline 56

Barro and Sala-i-Martin, Economic Growth.

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considerably for postmen. The coefficient of variation for postmen, however, can be reduced to 0.23 in the 1870s by removing just one outlying observation (Raipur) for 1875. Therefore, I do not put too much weight on the observed decline in the dispersion of the postmen wage series. In general, the initial degree of real wage dispersion persists in contrast to (and despite) the considerable decline in the coefficient of variation in food grain prices measured by Hurd (and also evident in the cost of living series underlying this paper’s real wage calculations). After breaking the sub-continent into five large regions, it appears that there may have been some ␴-convergence within the Central region and some divergence in the North.57 Again however, the large wage decline in just one district (Raipur) drives most of this convergence within the Central region. The widening dispersion in the North cannot be pinned easily on any one district. Evidence for ␴-convergence within the other regions is mixed, but it is certainly not the case that the lack of convergence at the national level is obscuring strong convergence at the regional level. Does a similar conclusion emerge from an examination of K. L. Datta’s wage series? I deflated Datta’s nominal wage figures for male agricultural workers with area-specific foodgrain price indices constructed from P&W to form real wage estimates for Datta’s ‘‘economic circles.’’ This exercise reveals no evidence of ␴-convergence from 1890 (when his figures begin) to 1908 (when my price indices end).58 In fact, the coefficient of variation of real wages for agricultural workers based on Datta rises from about 0.31 in 1890 to 0.39 in 1908.59 There are exceptions to this general lack of ␴-convergence, however. In the districts relatively close to India’s two most dynamic urban centers, Bombay and Calcutta, Figs. 1 and 2 reveal substantial convergence of real wage rates for artisans in the P&W data.60 This suggests that the forces of convergence may have been strongest in the country’s most commercially and industrially developed areas. But taking India as a whole, it is evident that neither the movement of goods

57

These regions approximate the census zones outlined in Visaria and Visaria, p. 491. See Appendix 2 for more discussion of the Datta wage figures. Datta only reports real wages in the form of time-series indices within ‘‘economic circles.’’ Therefore, there are no comparisons of real wages across regions in the Datta report. By taking Datta’s nominal wages, which are available in terms of money, and using the P&W price data (which is regarded as considerably more accurate than the wage data) to construct deflators for each of Datta’s ‘‘economic circles,’’ I was able to carry out some real wage comparisons. 59 These estimates are available from the author upon request. 60 Artisans’ wages in the Madras area also tend to converge, and in the Delhi area they converge up to 1900 after which Delhi’s wages rise above those of nearby districts. A convergence pattern is also evident in horsekeepers’ wages near Bombay and Calcutta, though not as strong as those for artisans. Agricultural wages were not recorded for Calcutta or Madras, but in the Bombay area there is not evidence of convergence, whereas in the Delhi area there is clearly convergence. 58

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FIG. 1.

Artisan real wages, Bombay area (Bombay ⫽ 100 in 1890).

nor the movement of people was sufficient to generate widespread ␴-convergence of wages in the late 19th century. Nonetheless, Danny Quah and Barro and Sala-i-Martin point out that it is possible for ␤-convergence to take place without compressing the overall dispersion of income: the convergence forces which tend to decrease income dispersion

FIG. 2.

Artisan real wages, Calcutta area (Calcutta ⫽ 100 in 1890).

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may be offset by random shocks which maintain the dispersion.61 The existence of district-specific random shocks implies that the steady-state dispersion of wages is greater than zero, and whether the dispersion of wages widens, narrows or remains the same over time depends on whether the initial dispersion starts below, above, or on par with the steady-state value.62 The steady-state value of the dispersion is a positive function of the variance of random disturbances and a negative function of ␤, the convergence coefficient. An agricultural economy with highly variable climatic conditions, like India, is likely to have a large variance of area-specific random shocks relative to agricultural economies in temperate regions and perhaps relative to more industrialized economies which have outgrown the whims of weather. This fact will tend to produce a wide steady-state dispersion of income in India relative to other places.63 Importantly, given an unchanging level of climate-dependent variance, the steady-state dispersion of income would narrow over time only if ␤ had increased. In theory, India’s transportation revolution could have increased ␤, the convergence coefficient, but the lack of ␴-convergence casts some doubt on this hypothesis. More direct evidence is sought below. Barro and Sala-i-Martin also show that unevenly spread aggregate shocks tend to raise the dispersion of income above what its long-run level would be in absence of aggregate shocks.64 India was subject to numerous large aggregate shocks, most obviously climatic disasters. These shocks might have maintained the dispersion of wages above its already high steady-state level despite underlying convergence forces.65 Given the absence of ␴-convergence, is there any evidence of widespread ␤-convergence occurring in India, and if so, how strong was it? Table 3 reports ␤-convergence regressions for real wages from 1873 to 1906. The basic form of 61 Quah, ‘‘Galton’s Fallacy’’; Barro and Sala-i-Martin, Economic Growth, pp. 384–387. Although Barro and Sala-i-Martin point out the theoretical possibility of ␤-convergence without ␴-convergence, none of the regional economies they examine (U.S., Japan, and Europe) exhibits a stable or rising cross section dispersion over long periods, and so India stands as an interesting counterexample. 62 Barro and Sala-i-Martin, Economic Growth, pp. 384–385, show that the steady-state variance of log income (␴2) can be written ␴2 ⫽ ␴u2/(1 ⫺ e⫺2␤), where ␴u2 is the variance of the random disturbance and ␤ is the convergence coefficient. 63 For example, citing the international comparative work of D. T. Walker, the Director-General of Observatories, Datta asserts that ‘‘of all countries which are dependent on agriculture, none has a rainfall so precarious as India’’ (Vol. 1, p. 61). George Boyer and Timothy Hatton calculate coefficients of variation of wages for England and Wales, France, Prussia, Sweden, and the United States (‘‘Regional Labour Market,’’ p. 100). The Indian dispersion is generally substantially higher than those in Boyer and Hatton’s sample. 64 Barro and Sala-i-Martin (Economic Growth, p. 386). If the aggregate shock is correlated with income, then it could either increase or decrease the observed dispersion of income depending on the sign and size of the correlation. 65 Time-series analysis of the coefficients of variation for agricultural labor, artisans, and horsekeepers indicates that the wage dispersion widened in years of famine. This relationship is less apparent when variance, rather than coefficient of variation (which is normalized by the mean wage), is the dependent variable.

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WILLIAM J. COLLINS TABLE 3 ␤-Convergence Regressions (1) Agriculture

(2) Artisan

(3) (4) Horsekeeper Agriculture

⫺0.0105 ⫺0.0110 ⫺0.0172 ⫺0.0106 (3.29) (3.59) (3.00) (3.25) Literacy 0.0000567 0.0000315 0.0000319 0.0000555 (4.77) (1.62) (2.06) (3.94) Price change — — — 0.00767 (0.80) Constant ⫺0.0102 0.001323 ⫺0.0110 ⫺0.0147 (4.39) (0.63) (4.14) (3.32) N 65 67 67 62 R2 0.17 0.16 0.28 0.21 Mean Dep. Var. ⫺0.00184 ⫺0.00144 ⫺0.00393 ⫺0.00224 Implied ␤ 0.0127 0.0135 0.0245 0.0129 ln (WInitial )

(5) Artisan

(6) Horsekeeper

⫺0.0116 (3.65) 0.0000407 (1.95) ⫺0.00796 (0.88) 0.00487 (0.95) 64 0.18 ⫺0.00170 0.0144

⫺0.0182 (3.18) 0.0000352 (2.07) ⫺0.00351 (0.58) ⫺0.00968 (3.06) 64 0.30 ⫺0.00404 0.0268

Note. The dependent variable is the average annual growth rate of real wages over the 1874 to 1905 period. Three-year averages centered on 1874 and 1905 are used as the starting and ending values, and the results are very similar if three-year averages are not used. Implied ␤ is the convergence rate and is calculated using the coefficient on ln (Winitial ) as described in the text. T-statistics are in parentheses. Sources. Wage data are taken from P&W. Literacy rates per 1,000 population are taken from the 1901 Census. The price change variable’s construction is described in the text and utilizes acreage data from Agricultural Statistics of British India and price data from Index Numbers of Indian Prices.

the equations is 1 T

ln

Wi,1905

( ) Wi,1874

⫽ ␣ ⫹ ␪ ln (Wi,1874) ⫹ ␥ Literacyi,1901 ⫹ ⑀i ,

where T is the number of years spanned, and W is a three-year average real wage centered on the designated year for district i. Following Barro and Sala-i-Martin, the annual convergence rate ␤ is defined as ⫺(1/T)1n (␪T ⫹ 1), where ␪ is the regression’s coefficient estimate on 1n(Wi,1874).66 In the first three columns of Table 3, ␤ varies between 0.013 (for agricultural real wages) and 0.025 (for horse-keepers).67 For the sake of comparison, the largest of these estimates is similar to that estimated by Barro and Sala-i-Martin for personal income among the United States from 1900 to 1920 (0.022), and is larger than that calculated by Williamson for real wages in his Atlantic economy sample in either the 1870 to 66 Barro and Sala-i-Martin (Economic Growth, p. 387) show that the average growth rate over a period of T years for area i can be written as follows: (1/T) 1n (YiT/Yi0) ⫽ a ⫺ [(1 ⫺ e⫺␤T)/T] 1n Yi0 ⫹ ui, where Y is income per capita, a is a constant, ␤ is the convergence rate, and u is an error term. 67 As in the ␴-convergence case, the large real wage decline for horsekeepers in Raipur has a sizable effect on the observed ␤-convergence for that series. Excluding Raipur changes the coefficient in the third column of Table 3 from ⫺0.017 to ⫺0.011. Potential adjustments to the standard errors out of econometric concerns regarding the ‘‘clustering’’ of district observations within economically homogenous areas turn out to be rather small. Note that a simple convergence regression run on the implied Datta real wage series for agricultural laborers does not reveal any evidence of ␤-convergence.

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1890 (0.012) or the 1890 to 1913 periods (0.008).68 The two smaller estimates of ␤ are still slightly larger than Williamson’s estimates but smaller than Barro and Sala-i-Martin’s estimates for the U.S. from 1880 to 1990 (0.017). Finally, all of the ␤ estimates for India are smaller than that calculated by Barro and Sala-iMartin for Japanese prefectures from 1930 to 1990 (0.0279).69 Coefficients of these magnitudes imply rather slow convergence of income toward the steady-state, with a half-life of anywhere from about 30 to 50 years. In a country with frequent and large climatic shocks, it is not difficult to see how these ␤-convergence forces could be offset in the measurements of the overall dispersion of wages. Splitting the sample into shorter time segments provides no evidence that ␤ increased over the period in question. The combination of no ␴-convergence and some ␤-convergence makes sense if the variance of areaspecific shocks was constant, the dispersion of wages started the period near its steady-state level, and there was no change in ␤ over time. In the first three columns of Table 3, each equation is conditioned by a measure of literacy.70 I used the province-level volumes of the 1901 Census to construct a measure of the number of literate people age 20 and older per 1,000 population in each district. Although the adult literacy rates in India were generally very low by western standards (the all-India rate was 74 per 1,000 population), the rates were considerably higher in cities than elsewhere (216 in Calcutta and Bombay). The United Provinces and Central Provinces had the lowest rates (42 and 39 respectively) whereas Burma had by far the highest literacy rate (295).71 The empirical relationship between literacy rates and wage growth might be confounded for a number of reasons: reverse causality, selective migration, and correlation with omitted variables. For example, aside from narrow conceptions of human capital, literacy might well signal the strength of an area’s commercial sector, exposure to outside social and economic influences, and so forth. Whatever the case, the literacy variable performs fairly well in the first three columns, and it continues to do so even when the most urban districts are excluded from the sample (results not reported).72 Education has not been well integrated into discussions of late 19th century Indian economic performance, especially at the regional level, and these results suggest that differences in human capital levels across districts might be an overlooked aspect of the subcontinent’s economic history.73 68

Barro and Sala-i-Martin, Economic Growth, p. 388. Williamson, ‘‘Globalization,’’ p. 280. Convergence studies of the more recent experiences of other Asian countries have found mixed results. See LaCroix and Southichak, ‘‘Output Convergence,’’ regarding Thailand and Saldanha, ‘‘Growth and Convergence,’’ regarding Indonesia. 70 Omitting the literacy variable leads to a smaller, but still statistically significant, coefficient estimate on initial real wages. 71 These literacy rates are simple averages of the male and female rates in each area. In general, however, male rates were far higher than female rates. The all-India male rate was 139 per thousand men over 19 years of age and only 8 per thousand women over 19 (Census of India 1901, General Report, p. 178). 72 Such excluded observations include Bombay, Calcutta, Madras, Secunderbad, and Rangoon. 73 Mukerji, History of Education, has studied the history of education in colonial India, but there is 69

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Another way of incorporating regional differences into this framework is through product price changes. To the extent that the districts produced goods in different relative quantities, exogenous price changes should have had different effects on each district’s aggregate income, and potentially on wages. I estimated the aggregate price increase of each district’s agricultural product (⌬P) with a weighted average of national price changes from 1873 to 1906. The weights (␣) were constructed from crop acreage figures for 1892–93 reported in the Agricultural Statistics of British India, so that for each district ⌬P ⫽ ⌺␣ipi, where the weight ␣i equals the acres of crop i divided by total acres cultivated in that district in 1892–1893, and pi is the percentage change in the price of crop i at the national level as reported in Index Numbers of Indian Prices.74 In general, districts in Burma, East Bengal, and Bengal had the largest product price increases over the period whereas those in Bombay, Punjab, and the Central Provinces had the smallest product price increases.75 This reflects that fact that rice and jute prices rose by considerably more than wheat or cotton prices in the late 19th century. Changes over time in the allocation of acres across crops within particular districts are not reflected in this measure; rather, the ⌬P measure is driven by changes in prices taking as given a particular year’s allocation of acreage. In theory, whether real wages should rise in response to an increase in product prices is ambiguous.76 Nonetheless, one can search for the potential wage effect empirically by including the measure of product-weighted price changes (⌬P) in a simple growth regression. These results are reported in columns 4–6 of Table 3. The product price change variable enters positively but insignificantly for agricultural wages and negatively but insignificantly for artisan and horsekeeper wages. Thus, there is no evidence that relative product price changes favored workers in some parts of India over others in the late 19th century. I made efforts to incorporate a number of other district characteristics into the wage growth regressions (results are unreported). The most interesting of these variables included the level of land revenue assessment (tax) in 1896–1897 which was extracted from Agricultural Statistics of British India 1892/93 to 1896/97 and the year in which a railway was built through each district which was determined from reports on Indian railways in the Parliamentary Papers. The tax variable, no connection made between relative levels of educational attainment among regions and economic performance. 74 Ideally, one would like to decompose each district’s produce into its various agricultural and industrial components. Due to data limitations, I am performing the price calculations using only agricultural products weighted by acreage rather than value of product. 75 These measures could not be constructed for Rajputana and Central India. 76 For example, in a simple two-country Ricardian model in which both countries have specialized production, a rise in the price of the good produced in Country 1 relative to that produced in Country 2 will lead to an increase in the wage level of Country 1 relative to that of Country 2. However, in a simple two-region Heckscher–Ohlin model with diversified production in each region, wages in both places should change by the same amount in response to an exogenous change in the price of some good. That is, there would be no correlation between the price change of each region’s product and each region’s wage growth.

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0.4199 (4.64) ⫺0.5431 (2.04) 0.8123 (5.00) 62 0.33 0.8859

Note. The dependent variable is the natural log of the artisan/agricultural laborer wage ratio averaged over three years centered on 1905. A similar three-year average centered on 1874 is the ‘‘initial’’ value entered in the regression. Using the ratios without logs produces similar results. T-statistics are in parentheses. Source. Relative wage figures are calculated using data from P&W. The price change figures are constructed as described in the text.

whether expressed in ‘‘per assessed acre’’ terms or ‘‘per capita in assessed areas’’ terms, does not come close to achieving statistical significance. The railway variable, whether the year itself is used in the regression or alternatively an indicator equal to one if the district built its first railroad after 1873, takes on a positive coefficient for each wage series but has a p-value below 10% for artisans only.77 Table 4 investigates changes in the wage gap between artisans and agricultural laborers in each district over time.78 One benefit of this focus on relative factor prices is that the cost of living deflator (and all of its potential flaws) will no longer influences the analysis. Looking across districts, the median ratio of artisans’ to agricultural laborers’ wages was approximately 2.3 at the period’s beginning and 2.5 at the period’s end, but different districts experienced quite different trends over time depending on both the initial measure of wage 77 The coefficient on the railway dummy variable is 0.001519 (t-stat. ⫽ 0.6) for agricultural labor, is 0.004062 (t-stat. ⫽ 1.7) for artisans, and is 0.002149 (t-stat. ⫽ 1.3) for servants. 78 The wage/rental ratio is also of great interest. Studies of land markets in Punjab (Mukerji, ‘‘Land Prices’’) and eastern India (Chaudhari, ‘‘Land Market’’) provide some quantitative information on land prices, but I have not found systematic and comparable data across India for the 1873–1906 period. The closest I have found is from Datta (Report, Vol. 3) who reports cash rents in Agra, Oudh, and the Central Provinces from 1890 to 1910. In this limited data set, comparisons of the implied wage/rental ratio across these areas does not suggest that relative factor prices converged.

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inequality and the estimated change in agricultural output prices. On average, districts in the Bombay and Madras provinces began the period with relatively high measures of the artisan/laborer wage ratio (2.9 and 3.2 respectively in the period 1873–1879), whereas those in Bengal started with a relatively low wage ratio (1.8). The coefficient on the initial wage ratio, which is significantly less than 1, reflects mean reversion of the wage ratio over time. The artisan/laborer wage ratio tended to decline in districts which started the period with a relatively high artisan/laborer wage ratio.79 This helps drive the coefficient of variation of the artisan/laborer wage ratio from 0.34 to 0.28 over the period. Thus, there is only slightly stronger evidence for relative factor price convergence than there is for real wage convergence across districts. The regression coefficient on the agricultural product price variable is also interesting. The more the acreage-weighted average price of a district’s agricultural produce increased, the more agricultural laborers tended to improve their position relative to artisans. Given the initial wage gap between artisans and agricultural laborers, a one standard deviation increase in ⌬P is associated with a 7.7% decline in the artisan/laborer wage gap over the period.80 CONCLUSION As with any study that attempts to characterize the whole of India, the results of this paper are accompanied by numerous qualifications and exceptions. Nevertheless, by pursuing comparisons across regions and over time, some important insights emerge. In general, late 19th century India was not characterized by a narrowing dispersion of wages across regions despite the period’s ‘‘transportation revolution.’’ There is, however, some evidence that the dispersion of real wages narrowed in the areas surrounding India’s premier commercial and industrial centers: Calcutta and Bombay. Furthermore, there is clear evidence that real wages in initially low-wage districts grew faster (or, more precisely, fell slower) than real wages in high-wage districts. When the analytical perspective shifts to examine the wages of artisans relative to those of agricultural laborers, one finds that the dispersion of these relative wages fell slightly over the period. Acreage-weighted changes in agricultural commodity prices, which were strongly influenced by external forces as India became more closely integrated into the world trading system, did not noticeably affect real wage growth within occupations, but they do appear to have had a positive impact on the wages of agricultural laborers relative to artisans. The results of this investigation help put into perspective the relative strengths 79 The regression reported in Table 4 produces the same results as one specified with the log change in the wage ratio as the dependent variable and the log initial wage ratio as an independent variable. In such a specification the coefficient on the initial ratio equals the coefficient reported in Table 4 minus one (i.e., ⫺0.5801). This is mechanically true, and the regression’s interpretation is identical. 80 The standard deviation of ⌬P is 0.141, and the 7.7% figure equals (0.141) ⫻ (⫺0.5431).

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of the forces acting on India’s economy. In theory, the ‘‘globalization’’ of the economy that accompanied the advances in transportation could have promoted wage equalization across districts by facilitating both more labor mobility and more interregional trade. In practice, large scale labor migration did not take place, and there is no evidence that the dispersion of real wages was moving from one steady state level to a narrower one over time. Within this context, the dominant forces acting on Indian workers’ earnings were the climatic shocks which frequently induced famine conditions and consistently precluded regional wage equalization. APPENDIX 1: SUMMARY STATISTICS TABLE A1 Summary Statistics for Agricultural Wage Convergence Regressions Variable

Obs

Mean

Std. Dev.

Avg. annual growth ln initial wage Literacy 1901 Price change

65 65 65 62

⫺0.00184 ⫺0.371171 78.78303 0.5343237

0.0106444 0.3123252 64.78194 0.1410248

TABLE A2 Summary Statistics for Artisan Wage Convergence Regressions Variable

Obs

Mean

Std. Dev.

Avg. annual growth ln initial wage Literacy 1901 Price change

67 67 67 64

⫺0.001437 0.4916579 84.12951 0.539694

0.010161 0.358210 71.18329 0.142155

TABLE A3 Summary Statistics for Horsekeeper Wage Convergence Regressions Variable

Obs

Mean

Std. Dev.

Avg. annual growth ln initial wage Literacy 1901 Price change

67 67 67 64

⫺0.0039281 ⫺0.2554112 84.12951 0.539694

0.0083696 0.2351851 71.18329 0.1421553

APPENDIX 2: WAGE DATA ACCURACY I offer three simple checks on the P&W data’s cross-sectional accuracy: a comparison of these real wages with estimates of agricultural output per rural capita in 1868–1869; a comparison of wage measures with those reported by K. L. Data which are only available for the period 1890 to 1912; and an investigation of

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whether observed migration patterns conform to measured real wage differences among regions.81 Alan Heston reports estimates of province level agricultural output per rural capita for 1868/1869. These data, like those in Table 1a–d, suggest that Bombay, the Central Provinces and Berar, and Punjab had relatively high incomes, whereas Madras and the United Provinces had relatively low incomes.82 Bengal finishes in the middle of Heston’s pack, but he combines Bengal, Bihar, and Orissa in a way that may well obscure substantial differences among these areas (roughly speaking, Heston’s figures for Bengal correspond to a combination of Table 1’s ‘‘Bengal’’ and ‘‘East Bengal’’ areas): there was a substantial net flow of migrants from Bihar and Orissa to eastern Bengal, suggesting that the relative ranking of these two regions (Bengal and East Bengal) in Table 1 is correct. Although there is no iron link between agricultural output per rural capita and real wages, the ordinal similarity of Heston’s figures and the real wage data suggests that the wage data are in line with what is known about productivity differences across regions in the country’s dominant economic activity. K. L. Datta harshly criticizes the reliability of the P&W nominal wage series in his Report. Direct comparisons of P&W’s nominal wage data with that compiled by Datta (for the 1890–1912 period) are difficult to make for the following reasons: P&W reports average monthly wages whereas Datta reports daily wages; P&W reports wages by district whereas Datta reports wages by ‘‘economic circle,’’ and although it is known in theory which districts fall into which circles, it is not revealed which districts actually provide data (and which do not); in reviewing the wage returns, Datta and his associates discarded observations regarded as ‘‘unreliable’’ but neither the criteria nor the discarded district observations are revealed; and Datta carefully separates urban and rural areas, but P&W does not. Direct comparisons of real wage figures are impossible because Datta does not have a real wage benchmark across his ‘‘economic circles’’; rather he reports real wage time series within circles. Nonetheless, it is fairly straightforward to check the rankings of nominal wages for agricultural laborers in various regions. Both P&W and Datta recognize Madras, the United Provinces, and the Central Provinces as relatively low (nominal) wage areas in the 1890–1906 period, and both sources also identify Punjab, Sind, and East Bengal as relatively high wage areas. P&W records Bombay as a relatively high nominal wage area, but Bombay falls closer to the average nominal wage level in Datta’s sample. Overall, the ordinal rankings across regions are fairly consistent in Data and P&W. When I convert Datta’s agricultural nominal wage estimates to real wage estimates using area-specific price data from P&W, however, I find some notable inconsistencies between the two data sets in the implied levels and trends of real wages. Most importantly, I find that relative to East Bengal P&W implies a much 81 82

Datta, Report. Heston, ‘‘National Income.’’

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higher real wage in the Gujarat (by 57%) and Deccan (by 40%) areas of Bombay than does Datta in 1890/1894. These gaps close somewhat by 1908 because Datta’s real wage series trend upward while the P&W series are stagnant or falling. Although the P&W and Datta real wage estimates for Madras are nearly on par (and low) in 1890/94, the P&W series drifts downward over time while the Datta series drift upward. Nonetheless, Sunanda Krishnamurty and Dharma Kumar have chosen to employ the P&W data in their specialized studies of Bombay and Madras respectively. As pointed out in the text, there is no evidence of ␴-convergence in Datta’s wage figures either in nominal or deflated (real) terms. The best estimates of internal migration have been prepared by Zachariah for each decade from 1901 to 1931. Of course migration is conditioned by many variables, but income levels are generally among the most robust predictors of migration flows. This relationship allows an indirect assessment of the validity of the real wage data. Between the 1901 and 1911 censuses, did migrants move away from what have been characterized as low real wage areas to high real wage areas? The largest net emigrating areas (relative to the population size) for which I have real wage data were Madras, Rajputana and Central India (combined), and the United Provinces, and these were also identified as among the lowest paying regions. The largest immigrating areas were Assam (in East Bengal), Burma, and the Central Provinces. Both East Bengal and Burma were characterized by high wages, but the Central Provinces were not so characterized in the period 1900 to 1906. However, a large proportion of the immigrants to the Central Provinces were from the even lower paying Central India and United Provinces areas. A regression of immigration rates (or immigration levels) on the province-level agricultural real wage estimates from Table 1a finds a strong positive association between the two variables, and once again, this lends support to the crosssectional validity of the real wage measures. Migration data also permit an assessment of the time-series reliability of the data. A simple regression of external gross emigration on a national real wage series for the 1874 to 1906 period suggests that a one percent fall in the real wage led to a 1.6% increase in external emigration (outside British India). The emigration series is from the Statistical Abstract Relating to British India (various editions). The real wage time series is constructed by calculating national nominal wage indices for agricultural labor, artisans, and syce (weighted by district populations), then taking a simple average of these nominal wage series to form a national series, and finally deflating with a national wholesale price index. OLS regression of the natural log of the number of emigrants on the natural log of the real wage series yields a coefficient estimate of ⫺1.59 and a t-statistic of 3.08. Cochrane–Orcutt estimation yields a coefficient estimate of ⫺1.20 and a t-statistic of 2.08. Visual inspection suggests that both series are stationary. This is necessarily a rather crude time series test, but it does suggest that on average the real wage time series relates to emigration as theory and empirical studies for other countries suggest that it should.

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Nevertheless, it is difficult to square the time series aspect of P&W with that of Datta. For example, confining the comparison to the period covered by both studies (1890 to 1906), Datta suggests that nominal wages for agricultural laborers in rural areas increased from an index value of 99 to 152, with almost all of the gain coming after 1895.83 A similar increase is recorded for artisans in both rural and urban areas and for general laborers in urban areas (domestic servants do not do quite as well). The national nominal wage index implied by P&W suggests that wages for agricultural laborers rose from an index value of 100 in 1890 to around 115 in 1906. Similarly small increases in the nominal wage indices were calculated for artisans, horsekeepers, postmen, and canal workers. These differences in nominal wage trends generate contradictory stories for real wage trends over the period 1890–1906, and it is clearly important that scholars recognize these difference and qualify their interpretations of the period’s wage data accordingly. Finally, the generally flat time-profile of real wages, punctuated by sharp declines in famine years, is consistent with the anthropometric evidence offered by Lance Brennan, John McDonald, and Ralph Shlomowitz for indentured emigrants from South India to Fiji. Their evidence shows that ‘‘while there was no increase in the height of recruits born between the 1860s and the 1890s, there was a steady decline in chest circumference of recruits born during these four decades.’’84 For the lower classes of Indian society, there is no evidence of an improving standard of living in the late 19th century. APPENDIX 3: FOOD PRICE DATA The price deflator is composed of three equally weighted food prices (retail): rice, wheat, and gram. Average annual price quotations are generally expressed in terms of rupees per maund (where one maund equals approximately 82 pounds). The annual averages are based on price reports that were collected every two weeks throughout the year. Rice, wheat and gram were selected because they are the most widely (but not universally) quoted grain prices. The data were compiled as described below. The text and Appendix 4 discuss some of the obvious limitations to this ‘‘market basket.’’ Unfortunately, similarly systematic district level price information is not available for other goods. Rice. Prices were quoted for every district in the wage sample. Wheat. Backerganj has no wheat price quotes, so it is assigned the average price for East Bengal. Chittagong and Sylhet have wheat quotes up to 1898 and 1891 respectively. Thereafter, proportional changes in their wheat prices are assumed to be equal to the average wheat price change for other districts in East Bengal. Similarly, Bardwan’s wheat price change between 1905 and 1906 is determined by the average for other districts in Bengal. Madras has no wheat quotes after 1893. The following formula is used to estimate wheat prices for each district in 83 84

Datta, Report, Vol. III, pp. 12–13. Brennan, McDonald, and Shlomowitz, ‘‘Trends,’’ p. 243.

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Madras, Pw,i,t ⫽ Pw,i,1893 ⫻ (Pr,i,t /Pr,i,1893) ⫻ [(Pw,N,t /Pr,N,t )/(Pw,N,1893 /Pr,N,1893)], where Pw is the price of wheat in district i at time t and Pr is the price of ragi in district i at time t. Pw,N,t and Pr,N,t are national averages. The second term (Pr,i,t/Pr,i,1893) adjusts the 1893 wheat price by the proportionate change in ragi prices in that particular district between 1893 and t. The third term ([(Pw,N,t/Pr,N,t) /(Pw,N,1893/Pr,N,1893)]) adjusts the price estimate for changes in the relative price of ragi and wheat at the national level. Gram. Madras is lacking quotes on gram after 1885. A similar adjustment formula is used as described above in estimating wheat price movements. In this case it is the initial gram prices which are extended beyond 1885 using the changes in the price of ragi within the district and changes in the price of ragi relative to gram at the national level. APPENDIX 4: ALTERNATIVE REAL WAGE CALCULATIONS The construction of an accurate cost of living deflator for Indian wages poses a serious problem. Workers produced and consumed different types of foodgrains across India, but I have used prices for a common basket of grains to proxy cost of living differences. Are the real wage measures and the inferences drawn about the dispersion of real wages robust to changes in the composition of the consumption basket? I pursue this question in four steps in Table A4 with the agricultural wage series. First, I use the available agricultural statistics to determine which areas grew which kinds of foodgrains. In particular, I focus on rice growing areas versus wheat growing areas, though a strong argument could be made that inferior grains should be also considered. Second, I calculate ‘‘rice wages’’ for areas that predominantly grow rice and ‘‘wheat wages’’ for places that grew significant quantities of wheat. The rice wages are then expressed relative to East Bengal’s average rice wage in the 1870s and the wheat wages are expressed relative to Punjab in the 1870s. Third, I calculate both rice and wheat wages for the United Provinces which grew considerable amounts of both rice and wheat. Finally, I adjust all of the wheat wages by a factor equal to the ratio of wheat wages to rice wages in the UP. Essentially, the UP is used to link the rice wage and wheat wage series so that if workers in a rice-growing area X can buy 10% more rice than those in the UP, and workers in a wheat-growing area Y can buy 10% more wheat than those in the UP, then this exercise would conclude that real wages in X are on par with those in Y. Ultimately, although the levels of relative real wages change when the basket of foodgrains is changed, the fundamental story about differences in wage levels from Table 1a remains intact. Madras and UP are low wage areas whereas East Bengal and Sind are high wage areas (as is Burma which is not shown). Bengal and Punjab’s wages are in between, and by the end of the period the Central Provinces are also a low wage area. Bombay’s acreage was devoted primarily to

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1880–1889

1890–1899

1900–1906

Rice Wheat Adjust Rice Wheat Adjust Rice Wheat Adjust Rice Wheat Adjust wage wage wheat wage wage wheat wage wage wheat wage wage wheat East Bengal 100 Bengal 68.7 Sind 121.1 Bombay 62.8 Madras 44.8 UP 45.3 61.4 Punjab 100 C.P. & Berar 97.0

45.3 73.8 71.6

121.0 86.8 119.3 67.6 53.3 48.0

60.1 105.2

48.0 84.0

86.3 68.6 106.3 61.8 39.7 41.2 49.3 90.0

87.8

70.1

58.2

41.2 75.2

93.1 67.2 100.9 59.9 37.1 37.2 48.7 95.1

37.2 72.6

48.6

54.7

41.8

Note. District wages are weighted by the 1891 population to form provincial averages. Rice wages are expressed relative to East Bengal in 1873–1879. Wheat wages are expressed relative to Punjab in 1873–1879. The ‘‘adjusted wheat wage’’ equals the wheat wage times the ratio of rice to wheat wages in the United Provinces in each decade. In 1892–1893, 76% of all acres cultivated with foodgrains in Assam and Bengal (East Bengal and Bengal) grew rice, 30% in Madras, 23% in Sind, 21% in the Northwest Provinces and Oudh (roughly the United Provinces), and only 8% in Bombay. In 1892–1892, 32% of all acres cultivated with foodgrains in Punjab grew wheat, 31% in the Central Provinces, 25% in Berar, and 13% of the Northwest Provinces and Oudh. Source. Real wages are calculated using data from P&W. Crop acreage is from Agricultural Statistics of British India.

millets (jowar and bajra), and so it is included in this exercise with a note of caution. Was ␴-convergence more pronounced within rice growing regions or within wheat producing regions? The coefficient of variation in real agricultural wages (where the deflator is the rice price) across the rice producing areas is 0.46 in the 1870s and 0.45 in the 1900s. The coefficient of variation rises from 0.32 to 0.36 across the wheat producing areas. Again, it seems unlikely that the lack of ␴-convergence found earlier in the paper is an artifact of the cost of living series. REFERENCES Agricultural Statistics of British India, 1892–93 to 1896–97. Calcutta: Superintendent of Government Printing. Barro, Robert J., and Sala-i-Martin, Xavier (1995), Economic Growth. New York: McGraw–Hill. Bhatia, B. H. (1969), ‘‘Terms of Trade and Economic Development: A Case Study of India, 1861–1939.’’ Indian Economic Journal 16, 414–433. Bhattacharya, D. (1965), ‘‘Trend of Wages in India, 1873–1900.’’ Artha Vijn˜a¯na 7(3), 202–212. Blyn, George (1966), Agricultural Trends in India, 1891–1947. Philadelphia: University of Pennsylvania Press.

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