rafmacalaba/datause-encoder-data huggingface dataset:
|Village Irrigation Rehabilitation Project||
|---|---|
|Mahaweli Ganga Development Project(03)||
|Mahaweli Ganga Development Project(03)||
|Mahaweli Ganga Development Project(03)||
|Mahaweli Ganga Development Project(03)||
|Mahaweli Ganga Development Project(03)||
|Mahaweli Ganga Development Project(03)||
|Mahaweli Ganga Development Project(03)||
|Mahaweli Ganga Development Project(03)||
|Small and Medium Industries Project(02)||
|Small and Medium Industries Project(02)||
|Small and Medium Industries Project(02)||
|Small and Medium Industries Project(02)||
|Industrial Development Bank of Pakistan Project(02)||
|Industrial Development Bank of Pakistan Project(02)||
|Cottage and Small Industries Project||
|Cottage and Small Industries Project||
|Cottage and Small Industries Project||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Education Project(02)||
|Power Project(07)||
|Power Project(07)||
|Power Project(07)||
|Power Project(07)||
|Telecommunication Project(04)||
|Telecommunication Project(04)||
|Telecommunication Project(04)||
|Irrigation Systems Rehabilitation Project||
|Irrigation Systems Rehabilitation Project||
|Irrigation Systems Rehabilitation Project||
|Irrigation Systems Rehabilitation Project||
|Tea Rehabilitation and Diversification Project||
|Tea Rehabilitation and Diversification Project||
|Tea Rehabilitation and Diversification Project||
|Baluchistan Minor Irrigation and Agricultural Development Project|Tetsuo Yaguchi|
|Baluchistan Minor Irrigation and Agricultural Development Project|Tetsuo Yaguchi|
|Baluchistan Minor Irrigation and Agricultural Development Project|Tetsuo Yaguchi|
|Baluchistan Minor Irrigation and Agricultural Development Project|Tetsuo Yaguchi|
|Baluchistan Minor Irrigation and Agricultural Development Project|Tetsuo Yaguchi|
|Baluchistan Minor Irrigation and Agricultural Development Project|Tetsuo Yaguchi|
|Baluchistan Minor Irrigation and Agricultural Development Project|Tetsuo Yaguchi|
# **2. Objectives of the Study**

The main objective of this study was to assess the impacts of JCLIS on farmers’ access to irrigation, cropping patterns, and the gross value of crop output. These impacts were measured in terms of a) the gross irrigated area across Kharif, Rabi, and Zaid (summer) seasons; b) the hourly cost of irrigation; c) the cropped area in Rabi and Summer seasons; d) the increase in the area under high-value crops, and e) the gross value of crop outputs of the beneficiary farmers in comparison with that of the control group. The second objective of the study was to assess farmers’ satisfaction with the JCLIS and to identify areas of improvement in the management and the performance of the irrigation systems.

# **3. Methodology**

# _3.1 Sampling_

The primary survey undertaken during the course of the study began in September of 2021, at a time when a substantial number of working JCLISs had already been installed. Of the 572 working JCLISs, 230 had been installed by a year prior, before September 2020. Ninety-three percent of these systems were concentrated in five districts: Gumla, East Singhbum, Hazaribagh, Khunti, and Lohardaga, which are spread across two of Jharkhand’s three agro-climatic zones. These five districts were selected for the primary survey, and within them, 50 lift irrigation systems were selected based on probability proportional to size (PPS) (Table 1). The sample was selected from irrigation systems that had 10 or more beneficiaries per system. These covered 13 blocks across the five districts.<sup>2</sup> The sample was stratified to include an equal proportion of older and newer systems as well as of solar and diesel pumps. However, 68 percent of the systems in the sample had just become operational in 2020, and only 6 percent used diesel pumps.

**Table 1. Distribution of complete systems by district**

|District Name|Population Proportion of Complete<br>Systems|Sample Proportion of Complete<br>Systems|
|---|---|---|
|Gumla|43%|49.2%|
|East Singhbhum|14.4%|20.2%|
|Khunti|12.6%|14.5%|
|Lohardaga|12.6%|10.1%|

> 2 5 systems were found not to be working when the survey team reached the field. They were replaced by 5 other functioning systems from the neighbouring areas.

4
# **Kenya**

||**Prevalence amon**|**g females 15-24(%)**|**Spectrum**|**estimates**|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|1.07|-|
|**1991**|-|-|1.53|-|
|**1992**|-|-|1.99|-|
|**1993**|-|-|2.24|-|
|**1994**|-|-|2.15|-|
|**1995**|-|-|1.80|-|
|**1996**|-|-|1.40|-|
|**1997**|-|-|1.07|-|
|**1998**|-|-|0.84|-|
|**1999**|-|-|0.70|-|
|**2000**|18.0|-|0.72|-|
|**2001**|13.1|-|0.66|-|
|**2002**|10.8|-|0.61|-|
|**2003**|9.1|5.9|0.58|-|
|**2004**|6.2|-|0.54|6|
|**2005**|6.8|-|0.52|14|
|**2006**|5.6|-|0.51|27|
|**2007**|-|-|0.52|38|
|**2008**|5.7|-|0.53|-|
|**2009**|-|4.5|0.49|51|
|**2010**|4.6|-|0.46|61|
|**2011**|-|-|0.44|72|
|**2012**|-|-|0.41|73|

<u>ANC data sources: Sentinel Surveillance of HIV & STDS in Kenya Report 2006 and Sentinel Surveillance of HIV &</u> Syphilis from Antenatal Clinics in Kenya Report 2010 <u>National DHS and AIS data sources: Kenya DHS 2003 and 2008-2009</u>

|**Study reference**|**Prevalence**<br>**among**<br>**females 15-24**<br>**(%)**|**Incidence**<br>**rate/100**<br>**personyears**|**Time frame of**<br>**study**|**Study/Trial**<br>**population**|**Region**|
|---|---|---|---|---|---|
|Brubaker et al., HIV<br>Med2011||4.6|2005-2007|Discordant couples|Kisumu|
|Chege et al., J Infect<br>DevCtries2012|17.24 (18-24)||2007-2008|Adult general<br>population|Kisumu|
|Van Damme, NEJM<br>2012||4.7|2009-2011|PrEP among women<br>18-35 (control)|Bondo|
|Baeten et al., NEJM<br>2012<br>ALPHA network study||1.9|2008-2010|PrEP in discordant<br>couples(placebo)|Eldoret, Kisumu,<br>Nairobi,Thika|

56
# **1. Introduction**

How are trade agreements and the international organization of production related? The recent wave of Preferential Trade Agreements (PTAs) has brought this question to the forefront of trade research and the trade policy debate. The key insight of this literature is that the “depth” of trade agreements is associated with the international fragmentation of production.<sup>1</sup> This paper adds to this line of work by looking at how the content of trade agreements, that is the specific provisions embedded in PTAs, relates to the way in which goods are traded internationally (i.e. within-firms or arm's length). The underlying idea is that “deep” trade agreements affect - and are affected by - firms' make-or-buy decisions, that is whether producers outsource to trading partners' suppliers or vertically integrate production processes with affiliates in foreign economies.

Trade agreements are usually thought of as reciprocal market access exchanges involving tariff cuts and the reduction of other border measures. But most modern day trade agreements contain provisions that cover a wide array of non-tariff measures, both at the border and behind-the-border. An incomplete list includes: technical barriers to trade (TBT) and sanitary and phytosanitary (SPS) measures, rules on investment and intellectual property rights (IPR) protection, provisions on anti-corruption, competition policy, labor standards, etc. While some of these areas are regulated at the World Trade Organization (WTO), recent PTAs tend to go beyond multilateral rules (see, WTO (2011) for detailed evidence). The literature refers to these new trade agreements as “deep” to distinguish them from traditional PTAs that focus only on market access commitments -sometimes referred to as “shallow”.

Similarly, while most non-experts tend to think of international trade as involving the exchange of final goods produced with (mostly) local inputs, trade has radically changed in the past thirty years in response to a growing international fragmentation of production processes. This phenomenon has been widely

> 1 See Lawrence (1996) and Baldwin (2011) for a discussion of the relationship between PTAs and the international fragmentation of production and Antràs and Staiger (2012) for a first formal model that combines offshoring and the design of trade agreements. A survey of the academic literature and of the policy debate is in WTO (2011).

2
# **III. Models of Vehicle Ownership and Commute Mode Choice**

Calculating the effect of the policies outlined above requires estimates of the price elasticity of demand for passenger transportation. In this section we describe models of mode choice which we estimate to produce short-run price elasticities of demand. In these models we treat the number, origin and destination of trips as fixed, and look at the impact of changes in the time and money costs of travel on mode choice. We also estimate joint models of vehicle ownership and mode choice.

Before presenting the models that we estimate, we examine mode choice in Mumbai. Tables 2 and 3 are based on a survey of 5,000 households in the Greater Mumbai Region conducted by Baker, Basu, Cropper, Lall and Takeuchi (2005).<sup>9</sup> Table 2 shows the main commute mode<sup>10</sup> to work for the two most important income earners in each household who work at a fixed location within the GMR. Table 3 shows the main mode used for work and non-work trips, based on travel diaries administered to the main earner in the household, a randomly chosen adult in the household, and a randomly chosen person between 16 and 21.

Several points are worth emphasizing. The first is that work trips constitute almost half of all trips made by adults in Mumbai. Indeed, work trips constitute 67.5% of all trips when trips are weighted by distance traveled. The second is that over half of all trips in Mumbai are made on foot. Approximately 45% of work trips are made on foot, and the percentage is even higher for other types of trips. Because our interest from the

> 9 The Great Mumbai Region is an area of approximately 437 sq. km. whose population in 2001 was 11.9 million.  Our analysis applies to the GMR and not to the Mumbai metropolitan area, with a population of over 18 million.

> 10 For multiple mode trips, the main mode is defined as the motorized mode in which the traveler spends the longest time. Walking and bicycling can be a main mode only if the trip is single mode trip.

10
**Table 7: Analysis of PEFA Dimensions by Capacity Requirement and Concentration**

||(1)<br>Concentrated|(2)<br>Deconcentrated|(3)<br>Concentrated|(4)<br>Deconcentrated|
|---|---|---|---|---|
|LGDPit (log income)|0.573***|0.687***|0.575***|0.686***|
||_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|
|LPOPit (log populatio)|0.220***|0.225***|0.267***|0.274***|
||_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|
|AIDit|1.004**|0.799|1.163**|0.969*|
||_(0.031)_|_(0.128)_|_(0.019)_|_(0.079)_|
|HIGHCAPj|-0.623|-1.134***|0.116|-0.383***|
||_(0.268)_|_(0.002)_|_(0.114)_|_(0.000)_|
|HIGHCAPj*LPOPit|0.048|0.046*|||
||_(0.185)_|_(0.057)_|||
|SMALLit|||0.298|0.363|
||||_(0.221)_|_(0.251)_|
|HIGHCAPj*SMALLit|||-0.074|-0.275**|
||||_(0.730)_|_(0.037)_|
|Time trend|YES|YES|YES|YES|
|Regional dummies|YES|YES|YES|YES|
|Number of observations|4,921|5,338|4,921|5,338|
|Number of clusters|151|151|151|145|
|Adj. R-Squared|0.020|0.033|0.020|0.033|

Notes: Dependent variable is discrete PEFA score. All regressions use Tobit estimation.

*** p<0.01, ** p<0.05, * p<0.1.  p-values in parentheses are based on robust and clustered standard errors. Constant is included but not reported.

# **6. Conclusions and Policy Implications**

Small PICs perform poorly in PEFA assessments relative to most relevant comparators. The poor performance of small PICs is driven by low scores against certain common PFM functions, including procurement, internal audit, and strategic budgeting, across all small PICs. The relatively low incomes of small PICs are an inadequate explanation for poor performance, with small PICs performing worse than other countries at similar levels of income. Population size, however, also exerts a significant influence on PEFA performance. Smaller countries face a significant size penalty in PEFA scores, taking account of differences in income. Overall, the weaker average performance of Small PICs can be entirely explained by their population sizes and levels of income.

A range of evidence suggests that population impacts on PEFA performance of small countries through the imposition of capacity constraints. Population size matters most for countries that have populations of less than 500,000. Population ceases to have any significant impact for countries with populations of between 500,000 and ten million. Because there are fixed capacity requirements associated with the basic functioning of a PFM system in any country, population impacts are most pronounced for very small countries which have inadequate skills among their 22
Rs. 125, the Rs. 100 provides roughly enough cash-on-hand to purchase one policy. The goal of this treatment is to test the sensitivity of insurance demand to liquidity constraints.

Second, we randomly assign ICRISAT insurance educators to receive an endorsement by the local BASIX LSA. Two-thirds of villages are designated as endorsement-eligible villages. Within these villages, the LSA endorses the insurance educators for half the visited households by briefly introducing the ICRISAT insurance educator, declaring them trustworthy and encouraging the household to listen.<sup>11</sup> The BASIX LSA does not help explain or sell the product and is instructed to leave before the ICRISAT insurance educator begins describing the product.<sup>12</sup> Given BASIX’s good reputation and high penetration rate, this LSA agent is well known and trusted among village households. In non-endorsed visits the ICRISAT insurance educator, who is unknown to the local villagers, visits the household alone.

Third, we randomize whether the household receives additional education about the measurement of rainfall in millimeters and its conversion into soil moisture. Farmers generally decide when to sow crops by measuring the depth of soil moisture in the ground at the onset of the monsoon. However, insurance contracts are instead set in terms of millimeters of rainfall. Table 3 shows only 23% of households can accurately indicate the length of a fixed number of millimeters. To improve understanding, for 350 households, we show the household the length of 10mm and 100mm using a ruler. The household is then presented a chart showing how 100mm of rain translates into average soil moisture for the soil type of their farm.<sup>13</sup> For the other 350 households, educators do not provide this information.

_Gujarat: Basic experimental design. –_ Field experiments in Gujarat were conducted in 2007, the year after the baseline survey described above. Unlike Andhra Pradesh, where interventions were implemented through household visits, in Gujarat, SEWA used several techniques to market rainfall insurance, such as flyers, videos, and discount coupons. We randomly varied the content of each of these three marketing methods at the household level.

> 11 This two-tiered assignment structure was implemented to measure possible spillovers of trust within the village. It also helped reduce the demands on BASIX staff time.

> 12 ICRISAT employees recorded the degree to which the BASIX LSA followed the instructions. Instructions were followed exactly in 56% of cases. For the remainder, 25% did not show up or stayed at the house for too short a time. The remaining 19% stayed for the duration of the visit. In private conversations after the sales period, BASIX LSAs had no recollection of which individuals they had endorsed and whether they had purchased insurance. 13 Based on time use surveys reported by the insurance educator team, this education was presented rather briefly (an additional two minutes relative to a standard household visit).

11
supervision and regular onsite supervision. The regional composition of the sample is provided at the bottom of the table.

|**Table 2: Sample Comparison, Supervised **<br> <br>|**Versus Unsupervised MFI**<br>|**s**<br>|
|---|---|---|
|**Variable**<br>**Faces Onsite**<br>**Supervision**|**Does Not Face Onsite**<br>**Supervision**|**Difference**<br>**in Means**<br> **_(t test_**<br>**_significance_**<br>**_at 95 CI)_**|
|NGO Dummy<br>0.112<br>(0.31)<br>Obs. 125|0.76<br>(0.42)<br>Obs. 120|Yes|
|Accepts Deposits Dummy<br>0.74<br>(0.43)<br>Obs. 124|0.14<br>(0.35)<br>Obs. 120|Yes|
|Individual-Based Lender<br>0.51<br>(0.50)<br>Obs. 125|0.2<br>(0.40)<br>Obs. 120|Yes|
|Total Assets ($US millions)<br>55.0<br>(317.0)<br>Obs. 117|10.1<br>(36.2)<br>Obs. 113|No|
|Financial Self-Sufficiency<br>1.05<br>(0.31)<br>Obs. 113|1.01<br>(0.37)<br>Obs. 115|No|
|Age<br>11.32<br>(9.3)<br>Obs. 125|8.71<br>(4.96)<br>Obs. 120|Yes|
|Average Loan Size (Relative to<br>Per Capita Income of Bottom<br>3.3<br>(4.9)|1.35<br>(2.55)|Yes|
|Quartile)<br>Obs. 125|Obs. 117||
|% Women Borrowers<br>63.59%<br>(27.4%)<br>Obs. 84|73.1%<br>(29.4%)<br>Obs. 118|Yes|
|% of Staff in Head Office<br>45.7%<br>(40.0%)<br>Obs. 68|32.5%<br>(32.4%)<br>Obs.96|Yes|
|% of Staff that are Loan<br>Officers<br>52.1%<br>(18.8%)<br>Obs. 120|58.7%<br>(14.5%)<br>Obs.119|Yes|
|Operating Expenses/Gross<br>Loan Portfolio<br>21.7%<br>(20.8%)|38.8%<br>(117.1%)|No|
|Obs. 124|Obs. 119||

Comparing the characteristics of supervised and unsupervised institutions indicates strongly that the assignment of prudential supervision is non-random. In Table 2, we compare MFIs that face onsite supervision with those that do not because that regulatory variable splits our sample roughly in half. More detailed descriptions of the distributions and construction of the all of the variables used in the analysis are found in 10
newspaper circulation, freedom of the press and a measure of autocracy does not change the basic results (see Tables 15a-b, 16a-b).

# Table 15a. Governance vs. Legal Origin, **FOIA** and Circulation

# _DATA SET: KAUFMANN, KRAA YAND ZOIDO-LOBA TON_

||How authoriti<br>n|esareselectedand<br>placed|Capacityof<br>implementso|thestateto<br>undpolicies|Respectofth<br>thecitizensf|e stateand<br>or the rules|
|---|---|---|---|---|---|---|
||Voice and<br>Accountability|PoliticalInstability<br>andViolence|Government<br>Effectiveness|Regulatory<br>Burden|Ruleof<br>Law|Graft|
|SocialistLegalCode|-.34*|-.03|-.66***|.79***|-.59***|-.75***|
||(-1.71)|(-.19)|(-3.53)|(4.35)|(-3.51)|(4.41)|
|FrenchLegalCode|-.04|-.19|-.18|-.05|-.37***|40e**|
||(-.26)|(-1.29)|(-1.36)|(-.54)|(-2.96)|(-2.85)|
|GermanLegalCode|.44|.28|.16<br>|-.40<br>|.41*<br>|.02<br>|
||(1.4)|(.94)|(.47)|(-1.6)|(1.9)|(.04)|
|ScandinavianLegalCode|*57***|*54***|*59***|-.08|.52***|.80***|
||(2.94)|(3.63)|(3.65)|(-.86)|(4.03)|(4.82)|
|Income|.23|35***|.41***|.01|.48***|53***|
||(1.56)|(3.25)|(3.44)|(.13)|(5.24)|(4.49)|
|Circulation|.11|.09|.04|.26**|.03|-.02|
||(1.33)|(1.28)|(.50)|(4.42)|(.43)|(-.34)|
|FOIA|.57***|.04|.29*|.19*|.12|.27*|
||(3.35)|(.32)|(1.92)|(1.74)|(.97)|(1.72)|
|Constant|-2.36**|-3.28***|-3.52***|-.80|-3.86|-4.17***|
||(-2.47)|(-4.35)|(4.62)|(-129)||(-5. 77)|
|#obs|103|101|101|3||101|
|RI|<br>_.55_|_.50_|.60|.56|.67|.68|

# Table 15b. Governance vs. Legal Origin, FOIA and Circulation

_*DATA SET: ICRG, Averaged over the period 1984-199 7_

||ICRG|Corruption|Ruleof<br>Law|Bureaucracy<br>Quality|Contract<br>Repudiation|Expropriation<br>Risk|
|---|---|---|---|---|---|---|
|SocialistLegalCode|.32<br>(I.00)|.64*<br>(1.79)|1.12***<br>(3.88)|.02<br>(.04)|.09<br>(.27)|49*<br>(2.08)|
|FrenchLegalCode|-.37**<br>(-2.01)|-.26<br>(-1.05)|-.25<br>(-.96)|-.68**<br>(-2.62)|-.20<br>(-1.23)|-.34**<br>(-2.24)|
|GermanLegalCode|79*<br>(2.00)|.76<br>(1.24)|.93<br>(1.57)|1.18**<br>(2.49)|.81***<br>(2.78)|.46*<br>(1.7)|
|ScandinavianLegalCode|.83***|1.47**|1.24***|*97***|.53**|.35*|
||(3.52)|(4.62)|(4.09)|(3.22)|(2.36)|(1.89)|
|Income|.32*|.40*|.60***|57**|.16|.17|
||(1.97)|(1.77)|(2.89)|(2.62)|(1.09)|(1.34)|
|Circulation|19**|.14|.19|_.05_|*33**|.27***|
||(2.06)|(.99)|(1.44)|(.41)|(3.68)|(3.52)|
|FOIA|.69***|79***|.58*|1.02***|.56***|42*|
||(3.15)|(2.81)|(1.89)|(3.41)|(2.93)|(2.38).|
|Constant|.45|-.41|-2.07|-1.17|1.36|1.98**|
||(.46)|(-.30)|(-1.62)|(-.88)|(1.52)|(2.48)|
|#obs|8.2|82|82|82|82|82|
||.67|.53|.58|.60|.66|.63|

_*indicates significance at_ **_.I_** _level,_ ** _at .05 level and_ *** _at .01 leveL_ 34
TABLE S-A Sensitivity Results for Fiscal Variables Regressions on Growth of Per Capita Income

|Beta|Stan.Err.|T-Stat<br>N|Re|Other Incl. Vars.*|
|---|---|---|---|---|
|Government Consumption|Share<br>(GOV)|1960-1989|||
|High<br>-1.621|3.355|48<br>78|-61|BMP,MIX,AFRICA|
|Low<br>~7.189|3.611|1.99<br>78|-59|LAAM, BMP, GDC|
|Government Consumption <br><br>|Share minus <br>|Def.<br>and Educ. Ex<br><br>|p<br>(GOVX<br>|)<br>1974-'989<br>|
|High<br>- 8.648|6.741|1.28<br>63|.62|STDD,AFRICA,LAAM?>|
|Low<br>21.024|7.482|2.81<br>59|-58|BMP,GDC,PI|
|Total Government Expen|diture Share|(TEX)<br>1974-1989|||
|High<br>- 2.750|2.055|1.34<br>67|.54|X, BMP, GDC|
|Low<br>-<br>7.784|2.135|3.65<br>82|-51|X,MIX, LAAM|
|CentralGovernmentSur|plus/Deficit|asShare<br>(DEF)|1974-19|89|
|<br>High<br>22.495|<br>4.863|<br><br>4.62<br>78|52|AFRICA,X,MIX|
|Low<br>13.044<br><br>|5.584<br>|2.34<br>65<br><br>|37<br>|STDD, BMP, REVC<br>|
|Low<br>5.297|6.181|86<br>53|.58|XTX,BMP,MTX|
|(with tax receipt v|ariables)||||
|Growth of Government C|onsumption Sp|ending<br>(GG)<br>1960-|1989||
|High<br>2271|.054|5.01<br>89|.66|REVC, BMP, GOV|
|Low(exXG)<br>106|052|2.03<br>85|64|GDC,STDD,X|
|Low(inXG)<br>.017|.012|1.41<br>95|.98|XG,XSG,GOV|

* INV, RGDP, GPO, and SEC included in all Growth regressions. Underlined variables show additions which make coefficient insig. or change signs

23 I€ one excludes the continent dummies, the extreme bound procedure chooses X, BMS, STDD as 2-variables. This yields a coefficient on GOVX of -13.5 and a t-statistics of 1.81, which is not significant at the 0.05 level.

39
**<u>Table A3. Household Level Fixed Effect Estimates for Impact of Land Tenure on Input Use Intensity (Tobit Model)</u>**

||Fertilizer|Pesticide|Seeds|Bullocks|Casual labor|Familylabor|
|---|---|---|---|---|---|---|
||||**Subsample with 5**|**0/50 sharing rule**|||
|Barga land|-782.389***|-120.547*|-1,149.474|-1,181.520*|-88.708|-4.078|
||(3.54)|(1.80)|(0.49)|(1.68)|(1.15)|(1.13)|
|Land size (acre)|-1,496.249***|-685.589***|-632.715|-626.246|-41.385|-78.094***|
||(3.25)|(3.22)|(0.45)|(1.59)|(0.27)|(6.59)|
|Distance to|-0.284**|-0.061**|-0.053|-0.320|-0.057|-0.021*|
|homestead (km)|(2.28)|(2.16)|(0.48)|(1.32)|(0.87)|(1.95)|
|No. of obs.(plots)|2565|2565|2565|2565|2565|2565|
||||<br>**Subsample with 7**|<br>**5/25 sharing rule**|||
|Barga land|-262.068***|-79.704**|-14.240|-124.090|-75.653|-5.675***|
||(4.16)|(2.34)|(0.18)|(1.51)|(1.13)|(2.84)|
|Land size (acre)|-350.555***|-137.839***|-284.088|-368.831**|-22.751|-30.949***|
||(3.31)|(2.94)|(1.59)|(2.12)|(1.30)|(2.89)|
|Distance to|0.010|0.003|-0.017|-0.013|0.049|0.000|
|homestead (km)|(0.47)|(0.38)|(1.30)|(0.53)|(1.19)|(0.22)|
|No. of obs.(plots)|2300|2300|2303|2300|2300|2300|
|_Notes:_All regressions<br>parentheses. *, **, and|are household level<br>*** denote signific|fixed effect tob<br>ance at 10%, 5|it estimates as expl<br>%, and 1%.|ained in the text. A|bsolute values o|f_z_- statistics in|

26