rafmacalaba/datause-encoder-data huggingface dataset:
on productivity and use of key inputs allow us to explore the extent to which these parameters differ between tenanted and owned plots by the same household. Beyond contemporaneous productivity impacts, we assess whether tenancy reduces incentives to invest in soil conservation and irrigation.

We find that levels of non-contractible input use and output are significantly lower on tenanted as compared to owned plots by the same household. The difference in revenue is estimated to amount to some 20%, with application of fertilizer and family labor lower by 28% and 11%, respectively, though no statistically significant impact is found on use of seeds, a more observable input. Using land under tenancy rather than full ownership also reduces investment incentives; tenanted plots are 26% less likely to have received investment in land improvements during the last 8 years and 7% less likely to have private irrigation attached to them. A conservative estimate for the total output loss due to tenancy is close to 25%; higher than most of the estimates in the literature. This is in line with the fact that hypothetical land prices for tenanted land are 43% below those for land unencumbered by such restrictions.

A direct implication is that, the benefits from land reform when it was introduced in the late 1970s notwithstanding, relaxing the constraints that lead to widespread sharecropping could increase productivity. Options to increase the productivity of West Bengal’s rural sector and thus ensure sustainability of land reform impacts may include support to transfer of full ownership to current users. Our analysis suggests that this would not require large subsidies as in the absence of other constraints tenants could pay out landlords’ interest and still be better off. Beyond West Bengal, our findings imply that policies limiting contractual choice will have costs in terms of foregone productivity. Research to explore lower-cost alternatives to achieve the objectives pursued by such measures and study of ways in which the contracting parties adjust to minimize observed efficiency losses would be desirable.

The paper is organized as follows. Section 2 sets the stage by reviewing land reform in India, highlighting the conceptual basis and empirical estimates of the ‘Marshallian inefficiency’, and laying out key hypotheses and estimation strategy. Section three provides descriptive evidence from a comprehensive listing of some 90,000 households and a detailed follow-up survey of about 2,000 owner-cum tenants in West Bengal. Section 4 discusses econometric results on productivity- and investment- effect of share tenancy in the cross section and, using household fixed effects, the owner-cum tenant sample. Section 5 concludes by drawing out implications for policy in West Bengal and beyond.

# **2. Motivation and analytical strategy**

Land reform can be justified socially if it increases incentive for investment in non-contractible inputs. Indeed, land reform in West Bengal, one of the few Indian states that decisively pursued implementation of this policy in the late 1970s, is widely credited with having caused a spurt of agricultural growth in the 2
woman, who was out of school at baseline – regardless of how long she had been out of school prior to the study as long as she had never been married and was between the ages of 13-22. This sampling procedure led to an average sample size of 5.1 dropouts and 16.6 school girls at baseline in each EA.<sup>12</sup> Out of these 3,805 young women, 1,225 girls in 88 randomly selected EAs were sampled to be part of the CCT program.<sup>13</sup> Treatment status was assigned at the EA level and therefore we cluster all standard errors to reflect this design effect. A household questionnaire (not unlike a Living Standards Measurement Survey, or LSMS) was administered to our entire core sample – both treatment and control – at baseline and follow-up, which were conducted 12 months apart. This survey, described in more detail below, includes information on household characteristics, school enrollment, sexual behavior, and social networks.

From December 2007 through January 2008 offers to participate in the CCT program were made to the selected girls in treatment villages. Of the 942 girls in the baseline survey to whom we attempted to make _conditional_ offers, 17 could not be located, 32 were deemed ineligible subsequent to the offer process, and one refused. Because we continue to code all 50 of these ‘non-compliers’ as treated, we effectively estimate the Intention to Treat Effect of the original intent to offer the treatment<sup>14</sup> . As

> 12 We chose to target these two groups separately to ensure that we had a significant number of dropouts in our sample. Treating all dropouts gives our study the statistical power to focus on a sub-population whose school enrollment rates are more sensitive to the offer to participate in the program.

> 13 283 of these girls resided in EAs where the offers for baseline schoolgirls were not _conditional_ on school attendance _,_ and, as such, are not part of the analysis for this paper. In addition, 629 girls in treatment EAs were randomly selected to not receive cash transfers in order to examine possible spillover effects of the program. These girls are also not part of the analysis in this paper. This leaves us with a final sample size of

> 2,893 girls at baseline, for whom the treatment is always _conditional_ on school attendance.

> 14 Due to uncertainties regarding funding, the initial offers were only made for the 2008 school year. However, upon receipt of more funds for the intervention in April 2008, all the girls in the program were

11
22 -

-

units. A full cash transaction being impossible, in the absence of mortgage loan they achieve divisibility by acquiring large units and subletting part of it.

49. Comparisons between the size distribution of income and the size-distribution of hoasing units indicate that the output of new housing units is indeed skewed and contains a large share of big units. The Korean share of housing investment in GNP is not particularly low, but the number of new units per thousand households is low. Making a parallel with West-Germany during the postwar period, the size of new Korean units was more than 20 percent larger than in Germany in spite of the fact thgy Korean per capita incomes were only a third or less of German income.

# C. Asset-Based Housing Purchases and Accentuated Housing **Cycles**

50. Because of the lack of mortgage financing, the acquisition of a new unit is heavily dependent on the sale of existing units. This asset-based financing accentuates boom-and-bust cycles, and renders the housing sector very sensitive to changes in the level of liquidity in the economy. The close correlation between changes in Ml and in new housing is very clear in Figure 6. Also, whiLe there has been a clear upward trend in the share of housing investment in annual GNP, as should be expected, year-to-year fluctuations have been very pronounced I (see Figure 7). Econometric analyses by Chung (1985) confirm these strong cycles.

51. The lack of mortgage finance in the housing market increases the dominance of the capital gains aspects of housing over the imputed rental value of a unit. When the stock market began to develop in the early 1980s and inflation fell below 3 percent, wealthy households that were already owners shifted their interest to financial equities. This was reflected in the drop in the average price of houses in major cities by 2.8 percent between December 1985 and December 1986. Confirming the long-term asset demand for occupancy of the remaining households (as opposed to short-term investment), KHB reported that the strongest demand is for middle size units. The average price for units over 40 pyong and for less than 25 pyong (3.3 m2) was softening while that for 26 to 39 pyong remained steady.

# D. Shortage of Units and Decline of the "Housing Supply Ratio"

52. The rental market is regressive in Korea because the ability to rent is based on accumulated wealth and not on current earning power. The very high ratio of chonsei to the market value of the unit rented explains why housing units are so often shared and the very poor

- 15/ After adjustment for purchasing power parity using the Kravis et al system for international comparisons of gross product and purchasing power. See World Bank (1978), Chapter 3.
# Table A1

## **Refugee population by origin, 1960‐2013 (end‐year figures)**

Figures include people in refugee‐like situations.

||**1990***|**1995**|**2000**|**2005**|**2010****|**2013**|
|---|---|---|---|---|---|---|
|**Origin**|||||||
|Total World|17,395,979|14,896,087|12,129,572|8,661,994|10,549,686|11,703,179|
|**Sub‐Saharan Africa**|5,680,989|5,558,551|3,741,420|3,065,266|2,773,292|3,670,630|
|‐ Central Africa and Great Lakes|620,578|2,262,594|1,089,506|1,048,459|878,475|933,636|
|‐ East and Horn of Africa|2,583,784|1,582,992|1,504,938|1,386,481|1,518,092|2,336,848|
|‐ Southern Africa|1,671,196|373,294|436,669|229,282|161,689|33,367|
|‐ West Africa|805,431|1,339,671|710,307|401,044|215,036|366,779|
|MENA|1,383,333|1,068,910|867,005|799,327|1,993,712|3,149,994|
|Asia and Pacific|7,319,098|4,063,523|4,852,589|3,335,340|4,417,746|3,906,584|
|(excl. Australia, Japan, New Zealand)<br>**Asylum**|||||||
|Total World|17,395,979|14,896,087|12,129,572|8,661,994|10,549,686|11,703,179|
|**Sub‐Saharan Africa**|5,903,606|6,198,725|3,835,423|3,170,811|4,124,942|5,563,641|
|‐ Central Africa and Great Lakes|1,031,190|2,544,072|1,309,418|970,283|628,361|516,006|
|‐ East and Horn of Africa|2,502,387|1,560,684|1,099,092|1,047,379|1,241,098|2,038,958|
|‐ Southern Africa|1,311,168|246,577|317,888|228,637|146,162|135,542|
|‐ West Africa|814,486|1,385,031|709,802|325,125|168,334|242,348|
|MENA|244,375|462,361|399,223|599,387|1,940,987|2,630,787|
|Asia and Pacific|8,225,783|4,267,480|4,950,728|2,949,480|4,011,529|3,544,493|
|(excl. Australia, Japan, New Zealand)<br>Americas (excl. North America)|1,194,874|93,858|37,851|37,749|373,867|381,949|
|Developed countries|2,169,013|4,401,927|3,370,739|2,574,225|2,063,460|2,249,002|
|**UNHCR‐Assisted IDPs**|||||||
|Total World|3666400|4662200|5998501|6616791|21055500|31098400|
|**Sub‐Saharan Africa**|88000|1344000|2309691|2479998|6154000|7301100|
|Asia & MENA|829400|1699100|1845194|1532307|9497800|16993000|
|Americas|‐|8000|525000|2000000|3672100|5368100|
|(excl. North America)|||||||
|Developed countries|2749000|1611100|1318616|604486|1731600|1436200|
|**Total IDPs**|||||||
|Total World|20285000|17580000|19298000|19027800|25111000|N.A.|

39
are the dominant seat of major administrative and economic functions.

Access to these urban growth centers can be used to de fi ne an integrated (I) and a less integrated (LI) region. One can use some access measures such as travel time to these cities to identify these regions. However, such measures are arguably endogenous because of the endogenous placement of road infrastructure. Instead we utilize the natural border created by two major Asian rivers, the Ganges and the Brahmaputra. These rivers sliced the country into three pieces (Figure 1). We de fi ne the I region as consisting of areas which are geographically contiguous to either Dhaka or Chittagong metropolitan area. The LI region on the other hand accounts for the territory that lies to the West of the Brahmaputra (Rajshahi Division) and South of the Ganges (Barisal and Khulna divisions, and a small portion of Dhaka division) rivers. The appendix Table A.1 shows that rural areas in the I and LI regions do not di ff er substantially in terms of some key infrastructure indicator (e.g. electricity coverage) except for the presence of di ff erent types of banks and distance to the capital city, Dhaka.<sup>12</sup> While there are di ff erences in the urban amenities between these regions, the most important di ff erence between the I and LI regions is that of the access to large and growing markets in major metropolitan areas. Because of a signi fi cant di ff erence in the fl ow of these rivers between the monsoon and dry seasons, unreliable water transportation and a virtual lack of bridges crossing the rivers,<sup>13</sup> year-round commuting for work across the LI and I is not feasible.

# 3.1 The Spatial Gaps in Living Standards

In order to provide a feel of the trends in our data, we start with the simple investigation of the gaps in living standards across regions during 2000 and 2005 in Bangladesh. Figure 2 displays the di ff erence in LRPCE between the I and LI regions for all of the expenditure quantiles from 5 to 95. In 2000, the I-LI gap in LRPCE has increased with an increase in the consumption quantiles. For instance, the gap has been about 10.7 percent at the 20th percentile and 19.2 percent at the 80th percentile. This implies that rich in the integrated region has been

> 12 Table A.1 is generated using the community surveys of HES 2000 and 2001. Since these surveys are conducted only in rural areas, the summary statistics in the table relate only to rural areas.

> 13 The only bridge crossing the Brahmaputra, the Jamuna bridge, had started to operate in 1999.

12
|n, other than LHM<br> _T_ = 1_, X_)_−E_(_Y |T_ = 0_, X_)<br>ors<br>Basic Regressors<br>Same Name<br>0.068**<br>0.066**<br>(0.031)<br>(0.031)<br>-0.027<br>-0.020<br>(0.018)<br>(0.016)<br>-0.006<br>-0.011<br>(0.025)<br>(0.025)<br>0.011<br>0.005<br>(0.029)<br>(0.028)<br>0.003<br>0.004<br>(0.022)<br>(0.021)<br>0.010<br>0.011<br>(0.008)<br>(0.008)<br>0.005<br>0.005<br>(0.012)<br>(0.012)<br>-0.017<br>-0.018<br>(0.014)<br>(0.014)<br>-0.022<br>-0.022<br>(0.018)<br>(0.017)<br> brackets. Columns 3-5 report the<br>omes and probit regression (13) for<br>lumn 4 includes controls for: Tigre<br>nclude a dummy =1 if village name<br>at village level and robust standard|
|---|
|a eliminatio<br>_E_(_Y |_<br>No Regress<br>0.031<br>(0.032)<br>-0.011<br>(0.020)<br>0.030<br>(0.031)<br>0.029<br>(0.039)<br>0.015<br>(0.035)<br>0.004<br>(0.009)<br>0.012<br>(0.014)<br>-0.006<br>(0.018)<br>-0.014<br>(0.021)<br> deviations in <br>ontinuous outc<br>cifcation in co<br>additionally i<br>s are clustered|
|to malari<br>Control<br>0.776<br>(0.417)<br>0.953<br>(0.212)<br>0.804<br>(0.397)<br>0.649<br>(0.478)<br>0.211<br>(0.409)<br>0.021<br>(0.143)<br>0.046<br>(0.209)<br>0.054<br>(0.226)<br>0.120<br>(0.325)<br>ith standard<br>on (12) for c<br>ls. The spe<br>n 5, controls<br>observation|
|conducive<br>Treatment<br>0.807<br>(0.395)<br>0.942<br>(0.234)<br>0.834<br>(0.372)<br>0.680<br>(0.467)<br>0.225<br>(0.418)<br>0.025<br>(0.156)<br>0.058<br>(0.234)<br>0.048<br>(0.215)<br>0.106<br>(0.309)<br>ol groups, w<br>g LS regressi<br>e any contro<br>on in colum<br>regressions,<br>0.1.|
|le B8: Robustness checks: Behaviours<br>keeps livestock_>_100m from home<br>overs stored water<br>does anything to prevent mosquito bites<br>mentions using net<br>mentions burning coils<br>mentions using spray<br>mentions burning animal dung<br>mentions burning herbs<br>mentions draining stagnant water<br> and 2 report means for treatment and contr<br>n treatment and control groups, estimated usin<br> The specifcation in column 3 does not includ<br>gion and sub-zone dummies. In the specifcati<br>from List 1 to List 4, and =0 otherwise. In all<br>d in parentheses. *** p_<_0.01, ** p_<_0.05, * p_<_|
|Tab<br>Variables<br>1. Household<br>2. Household c<br>3. Respondent<br>4. Respondent<br>5. Respondent<br>6. Respondent<br>7. Respondent<br>8. Respondent<br>9. Respondent<br>Note: Columns 1<br>difference betwee<br>binary outcomes. <br>tribe, Muslim reli<br>was not changed<br>errors are reporte|

20
23. Bender O, Och FJ, Ney H. Maximum Entropy Models for Named Entity Recognition. USA: Association for Computational Linguistics; 2003.Available from:

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24. Bhargava R, Zuckerman E, Beck L. CLIFF-CLAVIN: Determining Geographic Focus for News Articles; 2014. NewsKDD: Data Science for News Publishing.

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27. Malmasi S, Dras M. Location Mention Detection in Tweets and Microblogs. In: Hasida K, Purwarianti A, editors. Computational Linguistics. Singapore: Springer; 2016. p. 123–134.

28. Middleton SE, Middleton L, Modafferi S. Real-Time Crisis Mapping of Natural Disasters Using Social Media. IEEE Intelligent Systems. 2014;29(2):9–17. doi:10.1109/MIS.2013.126.

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31. Wahab L, Jiang H. A comparative study on machine learning based algorithms for prediction of motorcycle crash severity. PLOS ONE. 2019;14(4):1–17. doi:10.1371/journal.pone.0214966.

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13
45 = This can easily be derived from Pa. For o or, Thus, to calculate the amount of transfer needed to eliminate poverty in a perfect targeting scheme, one multiplies the poverty index PI by the poverty line and population n. For the universalistic scheme, each household receives an amount equal to the poverty line Z.<sup>39</sup> Note also that since the index is decomposable, it is possible to apply it to the various m household groups, such that where Ex, = 1.

Table 8 gives total transfers needed to eliminate poverty under a universalistic scheme using the benchmark incidence levels. It is calculated using the log-normal distribution 39 Since _Tu=nZ,_ **_P,_** can also be interpreted as the ratio of perfect targeting to universalistic targeting.
Table 1: Baseline Summary Statistics

||(1)<br>Con|(2)<br>trol|(3)<br>Treat|(4)<br>ment|(5)<br>_p_-value|(6)|
|---|---|---|---|---|---|---|
|Variable|Mean|SD|Mean|SD|(1)-(3)|N|
|**Panel A. Target child’s characteristics**|||||||
|<br>Female (%)|0.491|0.500|0.495|0.500|0.628|4,687|
|Age (months)|47.153|6.469|46.980|6.442|0.454|4,687|
|Attended preschool (%)|0.006|0.075|0.006|0.079|0.903|4,683|
|<br>Height-for-age z-score|-2.183|1.128|-2.098|1.174|0.179|4,609|
|<br>Total ASQ score|0.043|3.381|-0.041|3.349|0.605|4,682|
|ASQ Communication|-0.002|1.005|0.002|0.995|0.510|4,682|
|ASQ Gross Motor Coordination|-0.007|1.033|0.006|0.967|0.532|4,677|
|ASQ Fine Motor Coordination|0.002|1.005|-0.002|0.995|0.988|4,680|
|ASQ Problem Solving|0.033|1.007|-0.031|0.992|0.107|4,682|
|ASQ Socio-personal|0.013|0.991|-0.012|1.008|0.245|4,682|
|**Panel B. Caregiver’s characteristics**|||||||
|<br>Female (%)|0.840|0.366|0.833|0.373|0.569|4,686|
|Age (years)|31.969|8.924|31.874|9.223|0.991|4,666|
|Speaks Portuguese (%)|0.162|0.368|0.141|0.348|0.253|4,593|
|<br>No schooling (%)|0.444|0.497|0.457|0.498|0.502|4,660|
|Years of schooling|2.239|2.592|2.220|2.611|0.736|4,642|
|Is illiterate (%)|0.811|0.392|0.822|0.383|0.436|4,639|
|**Panel C. Household characteristics**|||||||
|Stimulation Index (Activities with children)|0.000|1.000|-0.064|0.998|0.497|4,687|
|Household Size|5.135|1.537|5.083|1.578|0.540|4,687|
|Number of children aged 0-18|3.143|1.419|3.068|1.419|0.268|4,687|
|Number of children aged 3-5 in HH|1.081|0.277|1.063|0.247|0.022|4,687|
|<br>Household receives remittances|0.113|0.317|0.098|0.297|0.264|4,678|
|Wealth index|-0.000|1.000|-0.013|0.931|0.550|4,687|
||F St|at.|_P_-Va|lue|N||
|Joint F-test|1.2|87|0.1|90|4,25|6|

_Notes: SD_ stands for _Standard Deviation_ and _N_ for the number of observations. _ASQ_ refers to 4. measures of skills from the standard Ages-and-Stages Questionnaire described in section 16
our panel sample, we can match firms with their banks<sup>10</sup> to identify the demand and supply on the SME financing market more accurately.

We also estimate the regressions using repeated cross-sections—to maximize the number of observations (countries and firms) and, thus, the statistical power for estimating the average crosscountry effect of Basel III implementation. However, the increased statistical power comes at the cost of not comparing identical firms before and after the treatment. We do not take the first difference when estimating the regressions for repeated cross-sections and include country- (and firm-) level fixed effects. Thanks to such broadening of the sample, our regressions for repeated cross-sections of firms contain more than 20,000 observations.

Basel III implementation may be nonrandom (Beck and Rojas-Suarez 2019) and our regressions may be susceptible to sample selection issues. To that end, we construct an inverse Mills ratio using a logit model that explains the probability that a country implements Basel III. We reestimate our baseline regression and the regression with heterogeneous impacts by firm and banking system characteristics, including the inverse Mills ratio to control for possible selection bias. The regression results are reported and discussed in the next section.

# **7. Regression Results: Basel III Implementation and SMEs’ Access to Finance**

This section reports and discusses our regression results. First, we investigate whether some quantitative factors could help explain the nonrandom implementation of Basel III in EMDEs, as highlighted by Beck and Rojas-Suarez (2019) and help us address possible selection bias in our main regressions.

Second, we present the baseline results for the repeated cross-sections and panel of firms estimated using the ordered logit model and adjusting for possible selection bias. For the panel of firms, we present the results with the matched bank-firm data as well as with bank systems characteristics.<sup>11</sup>

> 10 Not all firms report the name of the bank that granted credit to the firm.

> 11 In the latter case, firms are matched with the average bank indicators at the country level rather than with the individual bank that grants credit to the firm.

26