metadata: authors: 0: author: Berg,Claudia N.
1: author: Blankespoor,Brian
2: author: Emran,M. Shahe
3: author: Shilpi,Forhad J.
count: India,World
docna: 0: docna: Does Market Integration Increase Rural Land
            Inequality ? Evidence from India
docty: Policy Research Working Paper
abstracts: cdata!: Investments in transport infrastructure
            lower trade costs and lead to integration of villages with
            urban markets. Does spatial market integration increase land
            inequality in rural areas? Theoretical analysis by Braverman
            and Stiglitz (1989) suggests that the interactions of lower
            trade costs with credit market imperfections can increase
            land inequality. The primary mechanism is the adoption of
            increasing returns technology by large landowners facing
            lower trade costs which makes it more profitable to expand
            their scale by buying land from small, credit-constrained
            farmers. Using high- quality household survey data (the
            India Human Development Survey) on land ownership in rural
            districts of India, this paper provides the first evidence
            on the effects of market integration on land ownership
            inequality. It develops an instrumental variables approach
            exploiting two sources of exogenous variation: the location
            of a rural district relative to the Golden Quadrilateral
            network (an inconsequential place design) and the length of
            colonial railroad in the 1880s in a district (a historical
            infrastructure design). This paper discusses and deals with
            potential objections to the exclusion restrictions. The
            evidence suggests that a 10 percent increase in a gravity
            measure of market access increases the land Gini coefficient
            by 2.5 percent and the share of landless households by 6.8
            percent. This paper finds evidence consistent with the
            Braverman and Stiglitz (1989) hypothesis that the
            interaction of credit market imperfections with lower trade
            costs increases land inequality: a 10 percent increase in
            market access increases the adoption of increasing returns
            farming technology by 3.5 percent. There is a positive
            effect on land sales, but the instrumental variables
            estimates are imprecise. The robustness of the conclusions
            is checked by relaxing the exclusion restrictions using the
            Conley et al. (2012) approach, and the bias-adjusted
            ordinary least squares estimator of Oster (2019) that does
            not impose any exclusion restrictions. The estimated effects
            of market access cannot be accounted for by the colonial
            land revenue system, demographic pressure on land, and
            differences in inheritance law between the Hindu and Muslim
            population in a district.
display_title: Does Market Integration Increase
            Rural Land Inequality ? Evidence from India
listing_relative_url: /research/2023/01/33986890/market-integration-increase-rural-land-inequality-evidence-india
topicv3: Transport and Poverty m1326009 712,Infrastructure and Growth m1326244 1083,Spatial Inequality m1327181 1166,Growth, Poverty and Inequality m1327633 1091
publishtoextweb_dt: 2023-01-25T00:00:00Z
dois: 10.1596/1813-9450-10268
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 647328
1: author_key: 957135
2: author_key: NA
3: author_key: 84831
model_extractions: Policy Research Working Paper 10286 Does Market Integration Increase Rural Land Inequality?

# Evidence from India

_Claudia Berg Brian Blankespoor M. Shahe Emran Forhad Shilpi_ Development Economics Development Research Group January 2023
Policy Research Working Paper 10286

# **Abstract**

Investments in transport infrastructure lower trade costs and lead to integration of villages with urban markets. Does spatial market integration increase land inequality in rural areas? Theoretical analysis by Braverman and Stiglitz (1989) suggests that the interactions of lower trade costs with credit market imperfections can increase land inequality. The primary mechanism is the adoption of increasing returns technology by large landowners facing lower trade costs which makes it more profitable to expand their scale by buying land from small, credit-constrained farmers. Using high- quality household survey data (the India Human Development Survey) on land ownership in rural districts of India, this paper provides the first evidence on the effects of market integration on land ownership inequality. It develops an instrumental variables approach exploiting two sources of exogenous variation: the location of a rural district relative to the Golden Quadrilateral network (an inconsequential place design) and the length of colonial railroad in the 1880s in a district (a historical infrastructure design). This paper discusses and deals with potential objections to the exclusion restrictions. The evidence suggests that a 10 percent increase in a gravity measure of market access increases the land Gini coefficient by 2.5 percent and the share of landless households by 6.8 percent. This paper finds evidence consistent with the Braverman and Stiglitz (1989) hypothesis that the interaction of credit market imperfections with lower trade costs increases land inequality: a 10 percent increase in market access increases the adoption of increasing returns farming technology by 3.5 percent. There is a positive effect on land sales, but the instrumental variables estimates are imprecise. The robustness of the conclusions is checked by relaxing the exclusion restrictions using the Conley et al. (2012) approach, and the bias-adjusted ordinary least squares estimator of Oster (2019) that does not impose any exclusion restrictions. The estimated effects of market access cannot be accounted for by the colonial land revenue system, demographic pressure on land, and differences in inheritance law between the Hindu and Muslim population in a district.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fshilpi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
reflects affect efficiency affected of economic and political institutions in the countries colonized by European powers (Sokoloff Sokoloff (2000)), The effects of market liberalization depend on the trade costs which are largely determined Sokoloff and differences significantly.

appreciate the importance of trade costs by considering the polar case of extremely remote villages cut-off from effectively affected efficiency
profitability finance first effects effects spatial variation in market access helps to identify the effects of market integration. The effects
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“plausibly exogenous” approach significant (significant effect significant find find effects differences revenue systems across districts, differences in land reform policies across states, or differences fixed effects (significant effects, find effects
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<!-- Start of picture text --> effects<br>attempt to estimate the effects of the recent expansion (6 lanes) and improvements in the GQ<br>Because the effects of changes in trade costs due to the GQ expansion and upgrading<br><!-- End of picture text --> <!-- Start of picture text --> effects<br>different identification<br>Our analysis contributes to a large and growing literature on the effects of lower trade costs<br><!-- End of picture text --> (Faber (2014)), counters the effects of deindustrialization by increasing agricultural productivity <!-- Start of picture text --> effects<br>efficiency,<br><!-- End of picture text -->
a surge of interest in understanding the e�ects of the recent expansion and upgrading of the GQ network. Ghani et al. (2016) �nd evidence that GQ expansion and upgrading improved the organization and e�ciency of the manufacturing activities through sorting, scaling, and reallocation, by both the incumbents and the new entrants. Datta (2012) �nd that the �rms located closer to GQ bene�ted in the form of more e�cient inventory management. Das et al. (2019) provide evidence that GQ spurred �nancial depth in the districts along the GQ, especially in the districts with relatively better level of �nancial sector development to begin with. Abeberese and Chen (2021) �nd both �rm productivity rises and product scope falls as a result of the connection with the GQ highway. Estimates from a model of internal trade with variable markups calibrated to the Indian manufacturing sector suggest that real income increased by 2.7 percent as a result of lower trade costs from the expansion and upgrading of GQ (Asturias et al. (2018)). However, to the best of our knowledge, there are no studies that analyze the e�ects of market integration owing to lower trade costs on land inequality in India or any other country.

# (4) Empirical Issues and Identi�cation Strategy

We calculate a gravity measure of market access using travel time (in hours) in 1996, and population in a destination district in 1991 as an indicator of market size. Following Allen and Atkin (2016), our main market access measure uses a trade elasticity of 1.5.<sup>11</sup> For details on the construction of the gravity measure of market access, please see section OA.1 in the online appendix. As noted earlier, the focus on roads and highways re�ects the fact that roads and highways have become the main modes of transportation in India for both goods transport and traveling needs.

To understand the empirical issues, it is useful to consider the following triangular empirical model for estimating the impact of market integration on land Gini (LGini): 11The conclusions in this paper are robust to alternative choices of the trade elasticity parameter, and travel time in di�erent years. Please see the online appendix.

9
<!-- Start of picture text --> affect<br>effects<br>different differ<br>effects<br>effect<br><!-- End of picture text --> / effects effect coefficient on an economic outcome is biased upward (towards a substantial positive effect) because these
# (4.1) Colonial Railroads in the 1880s

The length of colonial rail track in the 1880s in a district is our �rst instrument for identifying the e�ects of market access of a district in 1996 on land inequality in 2012. We �rst discuss the plausibility of the exclusion restriction imposed. Then we explain why the colonial railroads are expected to be positively correlated with the market access in 2012 and report the relevant evidence.

As noted by Donaldson (2018), the locations of railroads built by the colonial government up to the 1880s were primarily dictated by defense considerations rather than economic objectives. The railroads were built and maintained by the military engineering core (National Transport Development Committee Report, vol 3, 2013, GOI). Following Donaldson (2018), we exclude the rail stations built after the 1880s as the Famine Commission report prompted the colonial government to target railroads to poor drought-prone districts more vulnerable to famine, thus making them potentially endogenous.

Some of the colonial railroads were �nanced by private railroad companies (Macpherson (1955)), and one might worry that they are likely to target the districts with higher economic potential to ensure adequate returns to the investors.<sup>12</sup> However, in colonial India, the privately �nanced railroads were guaranteed a 5 percent return by the government which ensured that the location choices were not driven by the imperative of ensuring a reasonable return to the investors. As a result, many of the private railroads were built in economically lagging districts. Hurd (1983, P. 743) writes: �..., many, if not most, of the unpro�table lines depended for their very existence upon the guarantee. Those earning less than 5 per cent included some of the lines in the north-west and in the Ganges valley, most of those in the Deccan, and all of the lines in Sind and south India. Thus, ... , had the guarantee not existed, it is unlikely that private capital would have invested in railways for large areas of India. These areas would, then, have had no rail service at all.� Another potential threat to the exclusion restriction is the possibility that the colonial railroads might have led to agglomeration and persistent e�ects. If historical rail stations created centers of commerce, they might lead to agglomeration economies and persistent

> 12British investors invested 95 million pounds between 1845-1875 (Macpherson (1955)).

11
growth effects even after rail transport became less important or train stations were abandoned.

affect significant the long-term direct effects of colonial railroads on effects reflect effects effects effect effect violation of the exclusion restriction have to be something different from these other transport the magnitude of the estimated causal effect of interest.
restriction, we take advantage of the Conley et al. (2012) bounds approach (see section 6.3 below).

The next question we address is that of relevance of the historical railroad as an instrument. Recall that our measure of market access is based on travel time through roads and highways. A natural question then arises: why should we expect colonial railroad to have a signi�cant correlation with the gravity measure of market access based on roads and highways? If colonial railroad is only tangentially related to the market access through roads and highways in 1996 (or 1988, 2004) then the IV estimates will be biased and unstable, and can yield implausible magnitudes (Stock and Yogo (2005)).

To check whether the historical railroad locations have systematically higher market access, we plot the kernel density function of market access for two samples, with and without a colonial rail station. Figure 1 shows clearly that the presence of a colonial railroad in the 1880s shifts substantially the density function of market access in 1996 to the right. The districts with colonial railroad have a higher mean and lower variance.<sup>14</sup> The �rst stage F statistics later in the IV regressions con�rm that the 1880 railroad length in a district has substantial power in explaining the variation in market access of districts in 1996.

There are a number of plausible reasons behind a signi�cant positive correlation between colonial railroads and current market access via roads and highways in Figure 1. To the extent transport infrastructure placement is determined by topography, we would expect a positive correlation between the placement of rail line and roads. Two topographical features are especially important for the placement of railroads: slope and curvature. The optimal rail track location tries to minimize the slope (for steep slope, going up hill requires more powerful locomotive, and braking is di�cult going down hill), and curves (a sharp curve reduces the maximum speed) (AREMA, 2003, Chapter 6). These two factors are also important for the choice of cost-minimizing road and highway routes.<sup>15</sup> Because of the topographical constraints, it is common to have rail tracks and highways placed close to each other. In fact, the old

> 14Districts with colonial railroad: mean=15.641 and variance=0.348. Districts without railroad: mean=14.911 and variance=0.372.

> 15The most widely used HDM highway planning model of the World Bank highlights the importance of slope (rise and fall) in choosing an �optimal' path for highways. See the discussion by Robinson and Thagensen (2004).

13
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justification) reflects differences The insight that deviations of roads from a linear network caused by topography offers credible identifying
Definitions unit of observation is the District defined from the 2001 census.

Our main indicator of land inequality is the Gini coefficient for land ownership in a district.

/ “area owned” variable Specifically,
<!-- Start of picture text --> For the GQ based identification scheme, we construct a hypothetical linear network connecting<br><!-- End of picture text -->

|/<br>Brinkhoff(2020).|
|---|

|Effects|
|---|

|fixed effects.<br>fixed effects<br>differences<br>different|
|---|
|coefficient<br>significant<br>weincludestatefixedeffectsandasetof agro-climaticcontrolsthatcanaffecttheproductivity|
|---|
<!-- Start of picture text --> effects<br><!-- End of picture text --> significant The estimated coefficients effects

# specification fixed effects. different The first two columns use 2SLS and rely on the 1880 railroad length (column 1) and Euclidean

efficient variation in our market access variable with first stage F statistics of 18.94 (land Gini regression) significant different first
identified identified Kolesar specification use the predicted market access from a zero stage to convert it to a just identified model.

significant reflects identified different.

effects
<!-- Start of picture text --> might worry that this “exact exclusion restriction” may be violated locally where an instrument<br>unspecified<br><!-- End of picture text --> <!-- Start of picture text --> relaxation of the exact exclusion restriction implies that we no longer have point identification,<br>effect<br>/<br>particular, we implement the “UCI” (union of confidence intervals) method proposed by Conley<br><!-- End of picture text --> specifies reflect
<!-- Start of picture text --> first<br><!-- End of picture text --> different effects differences differences land tax policies had long-term effects on land inequality in India.

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effects differences find affected significant effect estimates of the effects on financial deepening measured by formal bank branch (see panel C). different that are subject to increasing returns, and the effect is statistically significant at the 1 percent specification effect effects significant
<!-- Start of picture text --> briefly<br>market access does in fact help develop the formal financial sector in India.<br>thus suggests that the deepening of the formal financial sector is an important mechanism for<br>effects<br><!-- End of picture text --> <!-- Start of picture text --> effects<br><!-- End of picture text --> <!-- Start of picture text --> different<br><!-- End of picture text -->
restrictions imposed in the IV estimation by using the “plausibly exogenous” approach developed effect We find evidence that market integration increases the adoption of increasing The evidence on the effects of market access on land sales shows a positive

# Effectiveness

GarcAa-Santana, 113(1):15117(6):1881-1940.
<!-- Start of picture text --> Duflo,<br>, 95(4):1190-<br>101(2):274-298.<br>110:171—190.<br>99(3):435-448.<br>53(4):465—480.<br>115(2):389-430.<br>118:72-87.<br><!-- End of picture text -->
<!-- Start of picture text --> 2659-2772.<br>Market Integration, Reallocation, and Productivity Effects of Jamuna Bridge in Bangladesh.<br>Brinkhoff,<br>30(1):17-34.<br>94(1):260-272.<br>, 99(1):265-<br>firms.<br>99(1):46-57.<br>Shariff,<br><!-- End of picture text -->
|7(1):619-647.<br>108(4-5):899-934.<br>, 4(1):16-36.<br>81(2):681-724.<br>79(4):1407-1440.<br>95(2):682-697.<br>Effects<br>87(348):985-1015.<br>45(3):1125-1153.<br>108((4-5)):899-934.<br>39(6):23-66.|
|---|
|Toth,<br>126(591):317-357.<br>23(2):251-269.<br>25(15):1965-1978.<br>103(2):620-642.<br>Kolesar,<br>Identification<br>33(4):474-484.<br>Sokoloff,<br>Suffurage<br>Michaels, G. (2008). The Effect ofTradeon the Demand for Skill:<br>90(4):683-701.<br>coefficient<br>37(2):187-204.<br>90(4):643-665.|
|---|
- Ray, D. (1998). Development Economics. Pricneton University Press.

- Redding, S. and Turner, M. (2015). Transportation Costs and the Spatial Organization of Economic Activity. pages 1339�1398.

- Robinson, R. and Thagensen, B., editors (2004). Road Engineering for Development. Spon Press: Taylor and Francis Group.

- Singh, R., editor (1995). Grand Trunk Road: A Passage Through Time. Aperture Books.

- Sokolo�, K. L. and Engerman, S. L. (2000). Institutions, Factor Endowments, and Paths of Development in the New World. Journal of Economic Perspectives, 14(3):217�232.

- Todaro, M. and Smith, S. (2015). Economic Development. Pearson.

- van Maarseveen, R. (2020). The urban-rural education gap: do cities indeed make us smarter? Journal of Economic Geography, 21(5):683�714.

- Vanneman, R. and Barnes, D. (2000). Indian district data, 1961-1991: machinereadable data �le and codebook. Center on Population, Gender, and Social Inequality. University of Maryland.

- Verdin, K. L., Godt, J. W., Funk, C., Pedreros, D., Worstell, B., and Verdin, J. (2007). Development of a global slope dataset for estimation of landslide occurrence resulting from earthquakes. Technical report.

- World Bank (2018). The Web of Transportation Corridors in South Asia. Technical report, World Bank Group, Washington DC.

30
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**Table 1. Effects of Market Access on Land Inequality: OLS and Bias-Adjusted OLS  Estimates**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. var.|**ln**|**(Landgini 20**|**12)**|**ln**|**(Landless 20**|**12)**|
||||Bias-|||Bias-|
|Estimator|OLS|OLS|Adjusted|OLS|OLS|Adjusted|
|**ln(MA1996)**|**0.108*****<br>**(0.02)**|**0.111*****<br>**(0.02)**|**0.115*****<br>**(0.04)**|**0.360*****<br>**(0.07)**|**0.357*****<br>**(0.07)**|**0.352*****<br>**(0.11)**|
|ln(Slope)||-0.037|||-0.078||
|||(0.03)|||(0.09)||
|Elevation||-0.138|||0.148||
|||(0.14)|||(0.43)||
|Crop Suitability||-0.002|||-0.005||
|||(0.00)|||(0.00)||
|ln(Rain)||0.039|||0.153||
|||(0.05)|||(0.16)||
|ln(Temperature)||-0.188|||0.621||
|||(0.35)|||(0.96)||
|ln(Rain CV)||0.089|||0.256||
|||(0.13)|||(0.39)||
|ln(Temp. seasonality)||-0.056|||-0.064||
|||(0.08)|||(0.21)||
|Pop. density, 1961||0.023***|||0.037||
|||(0.01)|||(0.02)||
|Constant|-1.942***|-0.980||-6.262***|-11.059*||
||(0.25)|(2.08)||(1.14)|(5.84)||
|State Dummies|No|Yes|Yes|No|Yes|Yes|
|Observations|200|200|200|212|212|212|
|R-squared|0.166|0.380||0.167|0.428||

Note: Columns (1), (2), (4) and (5) are estimated by OLS. Columns (3) and (6) are estimated by Oster's biasadjusted OLS (Oster (2019)). Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
**Table 2A. Effects of Market Access on Land Gini (IV Estimates)**

|Dep. var.|(1)|(2)|(3)<br>**ln(Landgini 2012)**|(4)|(5)|
|---|---|---|---|---|---|
|Estimator|2SLS|2SLS|2SLS|Lasso-IV|2SLS|
|**ln(MA1996)**|**0.244*****|**0.232****|**0.241*****|**0.255*****|**0.197*****|
||**(0.07)**|**(0.09)**|**(0.06)**|**(0.06)**|**(0.05)**|
|ln(Slope)|-0.044|-0.043|-0.044|-0.044|-0.041|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|
|Elevation|-0.191|-0.186|-0.190|-0.195|-0.172|
||(0.15)|(0.15)|(0.15)|(0.16)|(0.14)|
|Crop Suitability|-0.003**|-0.003**|-0.003**|-0.003**|-0.003**|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|ln(Rain)|0.053|0.052|0.053|0.054|0.048|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.05)|
|ln(Temperature)|-0.428|-0.406|-0.421|-0.447|-0.343|
||(0.42)|(0.44)|(0.41)|(0.42)|(0.37)|
|ln(Rain CV)|0.138|0.134|0.137|0.142|0.121|
||(0.14)|(0.14)|(0.13)|(0.14)|(0.13)|
|ln(Temp. seasonality)|-0.024|-0.027|-0.025|-0.021|-0.035|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|
|Pop. density, 1961|0.015**|0.016*|0.016**|0.015**|0.018***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Constant|-1.908|-1.806|-1.877|-2.271|-1.506|
||(2.45)|(2.42)|(2.40)|(2.47)|(2.22)|
|First Stage||||||
|<br>ln(km of railroad)|0.117***||0.114***||0.192***|
||(0.03)||(0.03)||(0.07)|
|ln(dist. to GQ)||-0.128***|-0.120**||-0.015|
|||(0.05)|(0.05)||(0.08)|
|ln(dist. to GQ) x||||-0.131***|-0.112*|
|ln(slope)||||(0.04)|(0.06)|
|ln(dist. to GQ) x||||0.152|0.106|
|<br>Elevation||||(0.18)|(0.19)|
|ln(km of railroad) x||||0.002***|-0.001|
|<br>Crop suitability||||(0.00)|(0.00)|
|<br>Weak identification|18.94|6.25|21.93|9.19|34.23|
||0.0000|0.0134|0.0000|0.0000|0.0000|
|Observations|200|200|200|200|200|
|R-squared|0.239|0.263|0.247|0.216|0.321|

Notes: Columns (1) and (2) are estimated by two-stage least squares (2SLS) using railroad length and distance to the Golden Quadrilateral as instrumental variables, respectively. Column (3) is estimated using predicted Market Access as an instrumental variable. The predicted Market Access are fitted values from a zero stage with both railroad length and distance to GQ. The Lasso in column (4) is estimated using a parsimonious set of instruments chosen by Lasso from a broad set that include railroad length, distance to GQ, and each of their interactions with the exogenous control variables as instrumental variables. Lasso selects three instruments which are reported in the first stage in column (4). Column (5) is estimated using predicted Market Access as an instrumental variable. The predicted Market Access are fitted values from a zero stage including railroad length, distance to GQ, and all the instruments chosen by Lasso. In columns (3) and (5), under the first stage, we report the zero stage estimated coefficients of the instrumental variables. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
**Table 2B. Effects of Market Access on Landlessness (IV Estimates)**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dep. var.|||ln(Landless 2012)|||
|Estimator|2SLS|2SLS|2SLS|Lasso-IV|2SLS|
|**ln(MA1996)**|**0.974*****|**0.403***|**0.758*****|**0.678*****|**0.662*****|
||**(0.23)**|**(0.21)**|**(0.16)**|**(0.14)**|**(0.14)**|
|ln(Slope)|-0.079|-0.078|-0.078|-0.078|-0.078|
||(0.11)|(0.09)|(0.10)|(0.10)|(0.10)|
|Elevation|-0.106|0.129|-0.017|0.016|0.022|
||(0.50)|(0.41)|(0.44)|(0.42)|(0.42)|
|Crop Suitability|-0.010**|-0.005|-0.008**|-0.008**|-0.008**|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|ln(Rain)|0.120|0.151|0.132|0.136|0.137|
||(0.19)|(0.15)|(0.17)|(0.16)|(0.16)|
|ln(Temperature)|-0.530|0.534|-0.127|0.021|0.051|
||(1.34)|(0.99)|(1.12)|(1.06)|(1.03)|
|ln(Rain CV)|0.575|0.280|0.463|0.422|0.414|
||(0.50)|(0.38)|(0.43)|(0.41)|(0.41)|
|ln(Temp. seasonality)|-0.104|-0.067|-0.090|-0.085|-0.084|
||(0.26)|(0.19)|(0.22)|(0.21)|(0.21)|
|Pop. density, 1961|0.000|0.034|0.013|0.018|0.019|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Constant|-14.063*|-10.750*|-12.808*|-13.660**|-12.255*|
||(7.87)|(5.56)|(6.68)|(6.33)|(6.33)|
|**First Stage**||||||
|ln(km of railroad)|0.114***||0.114***||0.186***|
||(0.03)||(0.03)||(0.07)|
|ln(dist. to GQ)||-0.148***|-0.120**||-0.030|
|||(0.04)|(0.05)||(0.08)|
|ln(dist. to GQ) x||||-0.131***|-0.106*|
|<br>ln(slope)||||(0.04)|(0.06)|
|ln(dist. to GQ) x||||0.119|0.081|
|<br>Elevation||||(0.17)|(0.18)|
|ln(km of railroad) x||||0.002***|-0.001|
|<br>Crop suitability||||(0.00)|(0.00)|
|Weak identification|18.49|11.14|26.96|12.36|41.70|
||0.0000|0.0010|0.0000|0.0000|0.0000|
|Observations|212|212|212|212|212|
|R-squared|0.154|0.427|0.312|0.3535|0.361|

Notes: Columns (1) and (2) are estimated by two-stage least squares (2SLS) using railroad length and distance to the Golden Quadrilateral as instrumental variables, respectively. Column (3) is estimated using predicted Market Access as an instrumental variable. The predicted Market Access are fitted values from a zero stage with including both railroad length and distance to GQ. The Lasso in column (4) is estimated using a parsimonious set of instruments chosen by Lasso from a broad set that includes railroad length, distance to GQ, and each of their interactions with the exogenous control variables as instrumental variables. Lasso selects three instruments which are reported in the first stage in column (4). Column (5) is estimated using predicted Market Access as an instrumental variable. The predicted Market Access are fitted values from a zero stage with railroad length, distance to GQ, and the instruments chosen by Lasso. The predicted Market Access are fitted values from a zero stage including railroad length, distance to GQ, and all the instruments chosen by Lasso. In columns (3) and (5), under the first stage, we report the zero stage estimated coefficients of the instrumental variables. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
**Table 3. Relaxing the Exclusion Restrictions:  Conley et al.  Bound Estimates**

|||**Lower**<br>**Bound**|**Upper**<br>**Bound**|
|---|---|---|---|
|**ln(Landgini 2012)**||||
|ln(km of railroad)|θ ε[-0.01*_β_, 0.01*_β_]|0.097|0.399|
||θ ε[-0.05*_β_, 0.05*_β_]|0.023|0.502|
||θ ε[-0.1*_β_, 0.1*_β_]|-0.082|0.636|
|ln(dist. to GQ)|θ ε[-0.01*_β_, 0.01*_β_]|0.028|0.445|
||θ ε[-0.05*_β_, 0.05*_β_]|-0.033|0.541|
|Predicted Market Access 1|θ ε[-0.01*_β_, 0.01*_β_]|0.129|0.353|
||θ ε[-0.05*_β_, 0.05*_β_]|0.121|0.364|
||θ ε[-0.1*_β_, 0.1*_β_]|0.110|0.378|
|Predicted Market Access 2|θ ε[-0.01*_β_, 0.01*_β_]|0.140|0.371|
||θ ε[-0.05*_β_, 0.05*_β_]|0.131|0.383|
||θ ε[-0.1*_β_, 0.1*_β_]|0.120|0.398|
|**ln(Landless 2012)**||||
|ln(km of railroad)|θ ε[-0.01*_β_, 0.01*_β_]|0.441|1.549|
||θ ε[-0.05*_β_, 0.05*_β_]|0.157|1.991|
||θ ε[-0.1*_β_, 0.1*_β_]|-0.263|2.563|
|ln(dist. to GQ)|θ ε[-0.01*_β_, 0.01*_β_]|-0.068|0.876|
|Predicted Market Access 1|θ ε[-0.01*_β_, 0.01*_β_]|0.427|1.091|
||θ ε[-0.05*_β_, 0.05*_β_]|0.402|1.124|
||θ ε[-0.1*_β_, 0.1*_β_]|0.370|1.165|
|Predicted Market Access 2|θ ε[-0.0001, 0.0001]|0.375|1.022|
||θ ε[-0.05*_β_, 0.05*_β_]|0.351|1.053|
||θ ε[-0.1*_β_, 0.1*_β_]|0.320|1.092|

Notes: (1) θ is the direct effect of an instrument on the outcome variable. The lower and upper bounds are the estimated effects of market access on the relevant measure of land inequality given that θ belongs to a specified interval. (2) Predicted Market Access refers to estimated fitted values from a zero stage regression. This allows us to estimate a just identified IV model using predicted market access as a single instrument. (3) Predicted Market Access 1 is from a zero stage with both railroad length and distance to GQ. Predicted Market Access 2 is from a the zero stage with the three instruments chosen by the Lasso: distance to GQ interacted with slope, distance to GQ interacted with elevation, and railroad length interacted with crop suitability.
**Table 4. Understanding the Mechanisms**

|Estimator|(1)<br>OLS|(2)<br>OLS|(3)<br>Bias-<br>Adjusted|(4)<br>2SLS|(5)<br>2SLS|(6)<br>2SLS|(7)<br>Lasso-IV|(8)<br>2SLS|
|---|---|---|---|---|---|---|---|---|
|**Panel A**||||**ln(Tech. Use**|**2012)**||||
|ln(MA1996)|0.273***|0.235***|0.188|0.423***|0.289**|0.366***|0.350***|0.364***|
||(0.06)|(0.04)|(0.70)|(0.14)|(0.12)|(0.09)|(0.09)|(0.08)|
|First Stage|||||||||
|Weak Identification||||18.49|11.14|26.95|12.36|41.70|
|||||0.0000|0.0010|0.000|0.0000|0.000|
|R-squared|0.186|0.538||0.488|0.533|0.5136|0.519|0.514|
|**Panel B**||||**ln(Land Sale,**|**2012)**||||
|ln(MA1996)|1.172*|2.330**|3.093***|-1.537|1.902|-0.072|0.433|1.127|
||(0.66)|(0.90)|(0.96)|(2.65)|(3.13)|(2.04)|(2.11)|(1.90)|
|First Stage|||||||||
|Weak Identification||||18.49|11.14|26.95|12.36|41.70|
|||||0.0000|0.0010|0.000|0.0000|0.000|
|R-squared|0.014|0.163||0.080|0.162|0.131|0.143|0.155|
|**Panel C**||||**Credit: Form**|**al Bank**||||
|ln(MA1996)|0.017|0.014|0.011|0.067|0.064|0.066*|0.063*|0.057*|
||(0.01)|(0.01)|(7.07)|(0.04)|(0.04)|(0.04)|(0.04)|(0.03)|
|First Stage|||||||||
|Weak Identification||||18.49|11.14|26.95|12.36|41.70|
|||||0.0000|0.0010|0.000|0.0000|0.000|
|R-squared|0.005|0.340||0.282|0.318|0.314|0.317|0.322|
|State Dummies|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|212|212|212|212|212|212|212|212|

Notes: Columns (1) and (2) are estimated by OLS. Column (1) is a bivariate regression without any controls while column (2) includes state fixed effects and other controls. Column (3) is estimated by Oster's bias-adjusted OLS estimator. Columns (4) and (5) are estimated by 2SLS using railroad length and distance to GQ as IVs, respectively. Column (6) is estimated by 2SLS using predicted market access as the IV; which is estimated from a zero stage OLS regression including both railroad length and distance to GQ. Column (7) reports the Lasso estimates using a broad set of instruments that includes railroad length, distance to GQ, and each of their interactions with the exogenous control variables as IVs. The three IVs chosen by the lasso are: distance to GQ interacted with slope, distance to GQ interacted with elevation, and railroad length interacted with crop suitability. Column (8) is estimated by 2SLS using predicted market access estimated from a zero stage OLS regression including railroad length, distance to GQ, and all the lasso chosen IVs. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
# **Online Appendix**

## **OA.1 Construction of the Market Access Index**

Our Market Access measure is calculated as follows: where, MAit is market access of District _i_ at time _t_

- ttijt is the travel time (in hours) between Districts _i_ and _j_ at time _t_

Pjt _=_ population in destination District _j_ at time _t_ θ= trade elasticity To construct the Market Access measures, population and travel times were aligned as follows:

|MA|Travel Time|Population|
|---|---|---|
|MA1988|1988|**1991**|
|**MA1996**|**1996**||
|MA2004|2004|2001|

Note that for the main analysis, we focus on Market Access calculated using travel time from 1996 and population from 1991. We explore alternative travel times (1988, and 2004) as robustness checks. Following Allen and Atkin (2016) we use trade elasticity equal to 1.5, and report results using trade elasticity equal to 3.8 as well.

## **OA.2 Calculation of the GQ-based instruments**

Our identification strategy relies on two main sets of instrumental variables: one inspired by India’s Golden Quadrilateral (GQ) highways and the other by its colonial railways in the 1880s. Both are calculated using geospatial software. Here, we describe the calculation of the first instrumental variable.

For the first GQ IV, we construct a hypothetical linear network connecting the main cities targeted by the GQ project (see Figure 2). The target cities of the GQ include: New Delhi, Kolkata, Chennai, Mumbai and Bangalore. We construct two networks: one based on Euclidean distance and another “least cost path” derived from elevation and slope data. Our main results rely on the linear, Euclidean distance instrument but are robust to using the least-cost path instrument instead. It is also important to note that though the GQ highway project is a relatively recent investment, parts of it follows historical roads (Figure AF.1).

The least cost path is calculated based on a time cost raster method using the Cost Connectivity algorithm available in ESRI ArcGIS as the minimum time result from off road speeds on land. We construct the
time cost raster by assigning a speed to cross each pixel based on Tobler’s hiking function (1993) with a weight derived from historical land cover class (1900) following Ali et al. (2015). We use land cover from the HYDE model (Goldewijk et al. 2011) and we use slope data from Verdin (2007).

<mark>Hiking = 6 ∗ e</mark> −3.5 |s+0.05|∗0.6

where s is mean slope.

We then calculate the distance between each district’s centroid and each linear network. For robustness, we also consider the Euclidean distances from each district’s centroids to the actual GQ highways, which are available from Ghani et al (2014).

Similar to Faber (2014), we address the concern of non-random local route placements on the way between targeted city nodes by constructing a hypothetical network based on Euclidean distance and least cost path based on elevation and slope. Faber uses a simple land cover model based on the engineering literature (see Jha et al., 2001; Jong and Schonfeld, 2003; cited in Faber 2014) and includes measures of slope, development, water and wetland, where the algorithm prefers short and flat routes.
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# Major roads

## Selected cities

— ca. 1992 @® GQtarget cities — ca. 1872 BH Darjiling @ Patna
**<u>Table A.1. Using Market Access with travel times from other years:  1988, and 2004, Lasso</u>**

|**Table A.1, Panel A**: 1988|(1)<br>ln(Landgini, 2012)|(2)<br>ln(Landless, 2012)|(3)<br>ln(Tech use, 2012)|(4)<br>ln(Land Sale, 2012)|
|---|---|---|---|---|
|**ln(MA1988)**|**0.260*****|**0.732*****|**0.372*****|**0.495**|
||**(0.07)**|**(0.19)**|**(0.12)**|**(2.62)**|
|ln(Slope)|-0.033|-0.060|-0.065|-0.591|
||(0.03)|(0.10)|(0.05)|(1.13)|
|Elevation|-0.251|-0.148|0.177|3.611|
||(0.16)|(0.45)|(0.27)|(5.49)|
|Crop Suitability|-0.003**|-0.007*|-0.001|0.004|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.052|0.155|0.017|-1.143|
||(0.06)|(0.17)|(0.10)|(2.14)|
|ln(Temperature)|-0.552|-0.363|0.459|2.242|
||(0.42)|(1.13)|(0.75)|(15.62)|
|ln(Rain CV)|0.161|0.497|0.457*|10.056*|
||(0.14)|(0.43)|(0.26)|(5.73)|
|ln(Temp. seasonality)|-0.022|-0.060|-0.087|-4.483|
||(0.08)|(0.22)|(0.12)|(3.27)|
|Pop. density, 1961|0.016**|0.017|-0.009|-0.076|
||(0.01)|(0.02)|(0.01)|(0.33)|
|Constant|-1.725|-12.714**|-10.57***|-13.192|
||(2.33)|(6.29)|(3.88)|(89.47)|
|First Stage|||||
|ln(dist. to GQ) x Elevation|-0.258**|-0.310***|-0.310***|-0.310***|
||(0.11)|(0.11)|(0.11)|(0.11)|
|ln(km of railroad) x Crop|0.002***|0.002***|0.002***|0.002***|
|Suitability|(0.00)|(0.00)|(0.00)|(0.00)|
|Weak Identification|7.10|8.59|8.59|8.59|
||0.0011|0.0003|0.0003|0.0003|
|Observations|200|212|212|212|
|R-squared|0.210|0.322|0.511|0.146|
|**Table A.1, Panel B: 2004**|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ln(Landgini, 2012)|ln(Landless, 2012)|ln(Tech use, 2012)|ln(Land Sale, 2012)|
|**ln(MA2004)**|**0.277*****|**0.712*****|**0.382*****|**0.879**|
||**(0.07)**|**(0.17)**|**(0.09)**|**(2.35)**|
|ln(Slope)|-0.063**|-0.120|-0.097*|-0.656|
||(0.03)|(0.10)|(0.05)|(1.12)|
|Elevation|-0.170|0.054|0.272|3.612|
||(0.15)|(0.41)|(0.24)|(5.28)|
|Crop Suitability|-0.003**|-0.008**|-0.001|0.000|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.086|0.186|0.034|-1.114|
||(0.06)|(0.17)|(0.10)|(2.14)|
|ln(Temperature)|-0.491|-0.147|0.526|1.586|
||(0.44)|(1.08)|(0.66)|(14.85)|
|ln(Rain CV)|0.143|0.451|0.445*|10.237*|
||(0.15)|(0.42)|(0.24)|(5.65)|
|ln(Temp seasonality)|0.014|-0.036|-0.074|-4.464|
|.|(0.08)|(0.22)|(0.11)|(3.25)|
|Pop. density, 1961|0.017***|0.026|-0.006|-0.088|
||(0.01)|(0.02)|(0.01)|(0.31)|
|Constant|-2.983|-14.393**|-11.56***|-16.911|
||(2.65)|(6.53)|(3.77)|(90.32)|
|First Stage|||||
|ln(dist. to GQ) x ln(Slope)|-0.132***|-0.127|-0.127|-0.127|
||(0.04)|(0.04)|(0.04)|(0.04)|
|ln(dist. to GQ) x Elevation|0.162|0.111|0.111|0.111|
||(0.17)|(0.17)|(0.17)|(0.17)|
|ln(km of railroad) x Crop<br>|0.001***|0.001***|0.001***|0.001***|
|Suitability|(0.00)|(0.00)|(0.00)|(0.00)|
|Weak Identification|7.68|9.93|9.93|9.93|
||0.0001|0.0000|0.0000|0.0000|
|Observations|200|212|212|212|
|R-squared|0.181|0.331|0.524|0.151|

Note: All columns are estimated by Lasso and include state fixed effects. Weak identification and R-squared are from 2SLS using the IVs chosen by Lasso. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
**<u>Table A.2: Effects of Market Integration:  Market Access Measure Based on   trade elasticity 3.8</u>**

||(1)<br>ln(Landgini, 2012)|(2)<br>ln(Landless, 2012)|(3)<br>ln(Tech use, 2012)|(4)<br>ln(Land Sale, 2012)|
|---|---|---|---|---|
|**ln(MA1996),θ = 3.8**|**0.092****|**0.306*****|**0.182*****|**0.775**|
||**(0.04)**|**(0.11)**|**(0.07)**|**(1.07)**|
|ln(Slope)|-0.003|0.079|0.019|-0.210|
||(0.04)|(0.14)|(0.08)|(1.25)|
|Elevation|-0.560**|-1.117|-0.438|0.339|
||(0.27)|(0.75)|(0.42)|(7.08)|
|Crop Suitability|-0.003*|-0.008*|-0.002|-0.009|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|-0.099|-0.303|-0.257*|-2.334|
||(0.07)|(0.24)|(0.15)|(2.73)|
|ln(Temperature)|-1.452**|-3.052|-1.285|-7.621|
||(0.71)|(1.94)|(1.12)|(19.96)|
|ln(Rain CV)|0.403*|1.146*|0.880**|12.487**|
||(0.21)|(0.64)|(0.38)|(6.34)|
|ln(Temp. seasonality)|-0.133|-0.357|-0.265|-5.270|
||(0.09)|(0.29)|(0.17)|(3.37)|
|Pop. density, 1961|0.006|-0.018|-0.034|-0.241|
||(0.01)|(0.04)|(0.02)|(0.38)|
|Constant|7.254*|13.753|5.086|40.458|
||(3.78)|(10.45)|(5.94)|(113.36)|
|First Stage|||||
|Weak Identification|9.06|10.94|10.94|10.94|
||0.003|0.0011|0.0011|0.0011|
|Observations|200|212|212|212|
|R-squared|-0.126|-0.147|0.161|0.165|

Notes: Estimated by 2SLS using predict market access as the instrumental variable. Predicted market access is estimated from a zero stage including railway length, distance to GQ, and the IVs chosen by Lasso (distance to GQ x Pop. density, 1961; and railway length x Crop suitability). All columns are estimated by 2SLS and include state fixed effects. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
**<u>Table A.3: Estimates Controlling for the Colonial Land Revenue System</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ln(Landgini, 2012)|ln(Landless, 2012)|ln(Tech use, 2012)|ln(Land Sale, 2012)|
|**ln(MA1996)**|**0.278*****|**0.386**|**0.285****|**-0.403**|
||**(0.06)**|**(0.24)**|**(0.14)**|**(2.31)**|
|British control (yes=1)|-0.061|0.075|-0.020|2.926*|
||(0.04)|(0.13)|(0.08)|(1.60)|
|No landlord (yes=1)|-0.084|-0.019|0.074|3.497|
||(0.05)|(0.15)|(0.10)|(2.15)|
|ln(Slope)|-0.030|-0.078|-0.084*|-1.226|
||(0.03)|(0.09)|(0.05)|(1.21)|
|Elevation|-0.242|0.169|0.283|5.813|
||(0.16)|(0.42)|(0.25)|(5.52)|
|Crop Suitability|-0.003**|-0.005|-0.001|-0.001|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.039|0.146|0.028|-0.383|
||(0.06)|(0.15)|(0.09)|(2.21)|
|ln(Temperature)|-0.542|0.554|0.816|6.563|
||(0.43)|(1.04)|(0.67)|(15.37)|
|ln(Rain CV)|0.129|0.340|0.340|10.346*|
||(0.15)|(0.38)|(0.23)|(5.61)|
|ln(Temp. seasonality)|-0.042|-0.045|-0.090|-3.010|
||(0.08)|(0.21)|(0.12)|(3.38)|
|Pop. density, 1961|0.011|0.038|-0.006|0.098|
||(0.01)|(0.03)|(0.02)|(0.35)|
|Constant|-1.657|-11.039**|-10.019***|-46.566|
||(2.54)|(5.51)|(3.74)|(92.78)|
|First Stage<br>ln(dist to GQ) x ln(Slope)|-0.129***|-0.128***|-0.128***|-0.128***|
|.|(0.04)|(0.03)|(0.03)|(0.03)|
|ln(dist. to GQ) x Elevation|0.217|0.161|0.161|0.161|
||(0.18)|(0.17)|(0.17)|(0.17)|
|ln(dist. to GQ) x ln(Slope)|0.002***|0.002***|0.002***|0.002***|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Weak identification|8.05|10.96|10.96|10.96|
||0.0000|0.0000|0.0000|0.0000|
|Observations|200|212|212|212|
|R-squared|0.1756|0.3458|0.5119|0.1446|

Note: All columns are estimated by Lasso and include state fixed effects. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
**Table A.4:   Including population density from 2011 and proportion of Muslims as additional** **<u>controls</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. var.|ln(Landgini, 2012)|ln(Landless, 2012)|ln(Tech use, 2012)|ln(Land Sale, 2012)|
|**ln(MA1996)**|**0.273*****|**0.691*****|**0.327****|**-0.162**|
||**(0.06)**|**(0.15)**|**(0.13)**|**(2.37)**|
|ln(Slope)|-0.012|-0.003|-0.048|-1.318|
||(0.03)|(0.09)|(0.05)|(1.24)|
|Elevation|-0.217|-0.009|0.267|4.322|
||(0.15)|(0.40)|(0.24)|(5.27)|
|Crop Suitability|-0.003**|-0.007**|-0.001|-0.003|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.042|0.160|0.036|-0.874|
||(0.06)|(0.16)|(0.09)|(2.21)|
|ln(Temperature)|-0.478|0.083|0.774|4.327|
||(0.44)|(1.04)|(0.69)|(15.09)|
|ln(Rain CV)|0.166|0.371|0.333|8.771|
||(0.14)|(0.41)|(0.25)|(5.80)|
|ln(Tem seasonalit)|-0.012|-0.015|-0.073|-5.132|
|p. y|(0.08)|(0.20)|(0.11)|(3.24)|
|Pop. density, 1961|0.335***|0.716*|0.145|-9.044*|
||(0.12)|(0.37)|(0.25)|(4.78)|
|Pop. density, 2011|-0.172***|-0.373*|-0.081|4.828*|
||(0.07)|(0.20)|(0.13)|(2.59)|
|Muslim prop.|0.251***|0.989***|0.529***|-4.683*|
||(0.10)|(0.25)|(0.14)|(2.82)|
|Constant|-2.566|-14.956**|-10.655***|-3.339|
||(2.53)|(6.11)|(3.76)|(89.29)|
|First Stage|||||
|ln(dist. to GQ) x ln(Slope)|-0.101***|-0.102***|-0.102***|-0.102***|
||(0.04)|(0.03)|(0.03)|(0.03)|
|ln(dist. to GQ) x Elevation|0.067|0.049|0.049|0.049|
||(0.17)|(0.16)|(0.16)|(0.16)|
|ln(dist. to GQ) x ln(Slope)|0.002***|0.001***|0.001***|0.001***|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Weak identification|8.02|10.66|10.66|10.66|
||0.0000|0.0000|0.0000|0.0000|
|Observations|200|212|212|212|
|R-squared|0.2310|0.4024|0.5478|0.1567|

Note: All columns are estimated by 2SLS and include state fixed effects. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
# **-** **<u>Table A.5 :  Using Distance to Least Cost GQ Network as Instrument</u>**

||(1)|(3)|(5)|(7)|
|---|---|---|---|---|
|||||ln(Land Sale,|
|Dep. var.|ln(Landgini, 2012)|ln(Landless, 2012)|ln(Tech use, 2012)|2012)|
|**ln(MA1996)**|**0.188*****|**0.653*****|**0.334*****|**1.983**|
||**(0.06)**|**(0.17)**|**(0.10)**|**(2.05)**|
|ln(Slope)|-0.041|-0.078|-0.074|-0.607|
||(0.03)|(0.10)|(0.05)|(1.12)|
|Elevation|-0.169|0.026|0.264|3.094|
||(0.14)|(0.42)|(0.25)|(5.19)|
|Crop Suitability|-0.003**|-0.008**|-0.001|-0.010|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.047|0.137|0.008|-1.237|
||(0.05)|(0.16)|(0.09)|(2.15)|
|ln(Temperature)|-0.326|0.069|0.672|-0.343|
||(0.37)|(1.03)|(0.64)|(14.30)|
|ln(Rain CV)|0.117|0.409|0.414*|10.793*|
||(0.13)|(0.40)|(0.23)|(5.60)|
|ln(Temp. seasonality)|-0.037|-0.083|-0.098|-4.600|
||(0.07)|(0.21)|(0.12)|(3.21)|
|Pop. density, 1961|0.019***|0.019|-0.009|-0.165|
||(0.01)|(0.02)|(0.01)|(0.33)|
|Constant|-1.426|-12.200*|-10.039**|-27.137|
||(2.18)|(6.32)|(3.95)|(87.85)|
|First Stage|||||
|Weak Identification|24.9|28.46|28.46|28.46|
||0.0000|0.0000|0.0000|0.0000|
|Observations|200|212|212|212|
|R-squared|0.333|0.365|0.524|0.162|

Estimated by 2SLS using predict market access as the instrumental variable. Predicted market access is estimated from a zero stage including railway length, distance to least cost network, and the IVs chosen by Lasso (distance to GQ x Pop. density, 1961; and railway length x Crop suitability). All columns are estimated by 2SLS and include state fixed effects. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
# **Table A.6 Summary Statistics**

||**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|
|**Dependent**|||||
|Land Gini (index), 2012|0.78|0.13|0.46|0.99|
|Landlessness (%), 2012|56.36|24.71|4.29|98.63|
|Technology use (%), 2012|73.19|21.19|9.09|100.00|
|Land sale (rupees), 2012|495,037|1,427,056|0|12,900,000|
|**Treatment**|||||
|Market access (index), 1996|6,862,575|4,261,530|817,823|22,800,000|
|**Controls**|||||
|Mean Slope (%)|5.74|7.87|0.97|53.20|
|Elevation (1,000 meters)|0.35|0.35|0.01|2.94|
|Crop suitability index|63|19|1|100|
|Rainfall (millimeters)|1,151|678|209|4,157|
|Rainfall coefficient of variation|254|24|80|289|
|Temperature (Celcius)|120|21|57|156|
|Temperature seasonality (st. dev. Celcius)|4421|1687|926|7399|
|Population density, 1961|0.33|0.71|0.02|9.50|
|Number of observations|200||||