computerization and development: formalizing property rights … · 2018-02-26 · computerization...

Computerization and Development: FormalizingProperty Rights and its Impact on Land and Labor

Markets

Sabrin Beg1

January 2017

PRELIMINARY DRAFT

Abstract

I exploit an intervention in the land records management system of Punjabprovince of Pakistan, which resulted in computerization of approximately20 million land records that were previously recorded and maintained man-ually by 8000 local officers. Land records management service centers setup throughout the province allowed landowners and tenants to get con-firmation of their rights as well as conduct land transactions through anautomated process. This resulted in a formalization of property rights,potentially enhancing tenure security and facilitating market transactions.Using the staggered roll-out of the program in a difference-in-differenceframework I find that the program facilitated activity in the land, tenancyand labor markets. Rural households allocate labor away from agricultureand are more likely to substitute hired labor for family farm labor. Re-mittance income of households is higher indicating increased possibility ofmigrant members. I find that land markets respond even in short timesince the inception of the program—the program eases land market fric-tions as land sales and purchases are reported and the land Gini coefficientis significantly lower due to the program. Consistent with recent literatureI do not find a significant increase in credit market access.

Keywords: Property Rights, Rural Mobility, Agricultural Land Markets,Impact Assessment, ICT in Development

1University of Delaware (e-mail: [email protected])

1

1 Introduction

The vast majority of developing countries have no or excessively informal

system of property rights, a crucial economic institution of society. In Pun-

jab province of Pakistan, land rights are so informally defined that since

colonial times, records of 20 million landowners are held in old cloth bags

maintained by 8000 local officers or patwaris (historically appointed by the

British but the office has persisted). The inefficient and dispersed land

records system has led to tenure insecurity, with owners relying on the dis-

cretion of the patwaris for any transaction or proof of ownership. Land

transactions are relatively high cost (containing a high proportion of infor-

mal costs) resulting in land markets being thin. The low mobility of land

perpetuates the highly unequal distribution of land and, thus, livelihood

opportunities especially for the poor.

In 2009, the Punjab Government launched the Land Record Manage-

ment Information System to formalize and centralize land records of the

province. Through this program, which was phased out in stages across all

districts of the province, land records will be obtained from the patwaris,

computerized and made available to public at a central location in each

sub-district. While no titles are given out as part of the program, an owner

or tenant can go to a designated centre and obtain a government attested

copy of his ownership or tenancy status. Any mutation of land due to sale

or inheritance is conducted digitally at this designated centre. The pro-

gram thus represents an overhaul of an informal system that is replaced

with a more centralized and computerized system. Moreover, by making

land transactions automated the program reduces the influence of patwaris

and other local officers who initially acted as ‘middle-men’ in land transac-

2

tions. Formalizing the property rights institutions can improve security of

ownership and tenancy and has potential effects on land and labor markets,

specifically for the poor.

Theoretically, insecure property rights result in non-optimal invest-

ment, labor market inefficiencies, and credit constraints. In the existing

literature, the impact of improving tenure security has been challenging

to identify, as tenure security is far from random and is correlated with

personal and general institutional characteristics that determine labor and

other market outcomes. Field 2007, in her seminal paper, examines the

effect of a large land-titling program in Peru on labor market outcomes.

Galiani and Schargrodsky (2010) use a natural experiment to look at the

affect of land titling in Buenos Aires. These papers present examples of

‘hard touch’, and potentially expensive, programs that were focused on

urban areas. Aside from the high-cost aspect, the success of a universal

‘top-down’ land titling program is uncertain in the context of Pakistan—

such a program may be controversial amongst the deep-rooted, informal

systems in place and may also result in the powerful undermining the poor

in the race for rights. Deniniger, Ali and Alemu (2013) also note the infeasi-

bility of formal land titling in Africa. Ali, Deininger and Goldstein (2014)

find that a lower-cost land regularization program in Rwanda improves

land access for women and also has a significant impact on investment and

maintenance of soil conservation measures.

I evaluate the impact of a ‘softer-touch’ program, as in Ali, Deininger

and Goldstein (2014), but unlike other papers in the literature I am able to

focus on the effect in rural areas and on land markets. This is a significant

contribution as rural sector employs a large proportion of population in

3

many developing countries and there are concerns surrounding the widening

rural-urban divide and limited rural mobility, especially in South Asia.

Insecurity of tenure is one of the reasons that keeps households tied to

agriculture and limits labor market mobility. The thin land markets further

exacerbate the mobility of poor, especially in rural areas. I use the phased

roll out of the program to estimate its impact using a difference in difference

strategy on land and labor market outcomes. In doing so I also provide

novel evidence on the potential for ICT to improve governance and public

service delivery in low income and rural contexts.

I find evidence supporting the hypothesis that the program led to

improved tenure security, as households, specifically those in the lowest in-

come groups, are more likely to report land ownership and report higher

perceived value of owned properties. As expected the increased tenure se-

curity improves mobility into non-agricultural work for rural households.

The number of household members practicing agriculture as their primary

occupation is lower, and so is the share of income from agriculture. At the

same time income due to remittances, plausibly from migrant members,

is significantly higher. These results are particularly true for the poorest

households. Productivity of agricultural land is also better, indicating bet-

ter investments in land due to more secure property rights. Even in the

very short run (the program is just nearing completion) there are already

affects on the land market. Firstly, rural land inequality, as measured by

the gini coefficient, drops by 9%. Moreover, the poorest households are

more likely to rent out agricultural land and receive higher annual rents.

They are not more likely to rent-in land for agriculture, but the rent pay-

ments to landlords are still higher, signifying improved perceived value. In

4

future, I intend to utilize better data on land sales to examine if indeed

there was thickening of the land markets as results from survey data sug-

gest. From these results the program has significant potential in improving

rural mobility, which can advance structural transformation.

2 Related Literature

Property rights can affect productivity and efficient resource allocation

through two broad channels — limiting expropriation and facilitating mar-

ket transactions (Besley and Ghattak 2009). Empirically identifying the

effect of property rights is challenging as these rights are seldom randomly

assigned. Papers in the spirit of Fields (2007) and Do and Iyer (2008)

use difference in difference strategies in contexts with large land titling

programs, comparing areas or individuals with varying exposure to the

program before and after the program. Do and Iyer (2008) exploit a 1993

program in Vietnam that granted land titles (or Land-Use Certificates as

they are known in Vietnam) to all households. They find that households

in provinces that had made greater progress in land titling mildly increase

the proportion of cultivated area devoted to multi-year crops, indicating

longer-term investment. Additionally, households in provinces with more

land titles devote more time to non-farm activities. Field (2007) identifies

the effect of an urban titling program in Peru using a similar methodol-

ogy, finding that better ownership rights results in a substantial increase

in labor hours, a shift in labor supply away from work at home to work

in the outside market, and substitution of adult for child labor. Exploit-

ing a natural experiment in the allocation of land titles to urban squatters

in Buenos Aires, Giliani and Schargrodsky (2010) find improved property

5

rights increases housing investment as well as human capital investment.

All of these papers use widespread allocation of titles, a ‘hard-touch’ inter-

vention, finding evidence for the ‘limited expropriation’ channel.2.

Another body of literature notes the persistent nature informal and

communal property rights, specifically in the African context where land ti-

tling has had limited success; large-scale, top-down land-titling efforts have

not been successful due to the intricate nature of primary and secondary

rights, and asymmetric power distribution that in turn determines tenure

security (Goldsteing and Udry 2008, Deininger, Ali and Alemu 2011). Due

to limited success and scope of formal titling and with increased demand

for land in Africa, more recently governments have focused on low cost

and more feasible programs formalizing property rights, without offering

explicit land titles – Deininger, Ali and Alemu (2011) examine land certi-

fication in Ethiopia, while Ali, Deininger and Goldstien (2014) land regu-

larization in Rwanda. Formalizing property rights, similar to explicit land

titles, also seem to improve investment.

There is less conclusive evidence for ‘facilitating market transactions’

channel – Field (2005), Do and Iyer (2008) and Giliani and Schargrodsky

(2010) do not find that titling significantly improves credit access, while

Carter and Olinto (1996), Lopez and Romano (1997), and Alston et al.

(1999) find that they do. Lanjouw and Levy (2002) find evidence that weak

property rights increase transaction costs in rental and sale markets. Feder

and Feeny (1991) find a significant higher land value for titled land versus

squatters’ in Thailand, suggesting improved rights security can grease the

wheels of the land market.

2Other papers including Feder et al. (1988), Besley (1995), Banerjee et al. (2002), and Field(2005) also provide evidence that titles increase investment

6

I make a few contributions to this wide body of empirical literature on

property rights by looking at the computerization of land records in Punjab

province of Pakistan. Firstly, there is little or no examination of property

rights in the context of South Asia, where land rights, particularly in rural

areas, are haphazardly maintained and indeed persistently informal. Agri-

cultural participation is still pretty high in South Asian countries - 50%

of total labor force in India, 45% and 48% in Pakistan and Bangladesh,

respectively (compared to 24% for middle income countries) (World Bank

Data 2013). On the other hand agriculture accounts for just 18% of the

GDP on average for South Asia.3 Consistent with the high participation

in agriculture, the average proportion of rural population in South Asia is

67% of the total, lower by 16 percentage points in the 50 years since 1960.4

The rural-urban transition is relatively slower compared to Latin America

for example, where share of rural population fell by more than half from

51 to 20% between 1960 and 2013. Indeed, many have noted that despite

accelerating economic growth, the structural transformation in India and

South Asia has generally been slow (Binswanger-Mkhize 2012, 2013). Im-

proving tenancy security and rights of land use, as I explore in this paper,

can potentially stimulate this rural-urban transformation. In this paper, I

focus on labor and land market outcomes, shifting the attention away from

investment outcomes which have been documented widely, and focusing

more on the mechanisms that bring about this effect. This allows me to

speak to structural transformation and rural-urban linkages in the context

of Pakistan.

Secondly, as in the case with Africa, the existing complexity of land

317%, 25% and 16% for India, Pakistan and Bangladesh, respectively.461% of the population is rural in Pakistan, down from 78% in 1960

7

rights in Pakistan, lack of information on part of the citizens as well as

authorities, and discrepancies in distribution of power in a rural context

where land rights and power are connected (Beg 2016), make it infeasible

and costly to implement a universal land titling program. The program

in Punjab that converts the manual paper-based land records into a com-

puterized database is a vital improvement in the land record management

system that is a key precedent for well-defined property rights. As Roth

and McCarthy (2013) note formalization does not mean formal land titling

and registration; rather there is a continuum of land rights formalization

that extends from strengthening tenurial rights in law to better commu-

nicating those rights to land holders to resolving conflicts associated with

rights clarity to strengthening informal land leasing arrangements and con-

tracts to formal titling and registration within both individual and group

contexts. The Punjab Land record computerisation program is a more ‘soft-

touch’ intervention, as opposed to the titling programs in Peru and Viet-

nam above, resulting in a formalization of property rights through better

clarity of rights and facilitation of market transactions through automated

means rather than through corrupt middlemen. Moreover, the program is

most relevant to rural areas, in contrast with urban, squatter-related titling

programs in other contexts as discussed above.

Lastly, the role of ICT in governance and public service delivery holds

great promise for lower income nations (Banerjee and Sachin 2003, Baner-

jee and Ghosh 2006). Banerjee et al. (2014) find that disbursing program

funds through an electronic method reduces the number of administra-

tive tiers involved and consequently lowers leakages of public funds. The

land record computerisation program similarly automates the land transac-

8

tion process eliminating the role of officers and administrative departments

whose involvement made the process prone to corruption and delays. Thus

the paper also illustrates the use of ICT to promote market development

and repress corruption instances.

3 Background

Pakistan’s land administration system is inherited from the British colo-

nizers, who formulated rules and regulations regarding sale, purchase and

use of land resources to facilitate land tax collection. Land Revenue was a

major source of income for the state and the maintenance of land records

formed a prerequisite for that. A cadastral map, or Massavi of villages was

prepared, outlining land parcels to identify the land revenue payers. These

maps were primarily maintained by the surveyor or village administrator,

the patwari, who also made temporal changes like splitting and merging

of land parcels. The ‘Record of Rights’ was additionally compiled to sup-

plement the cadastral records showing existing ownership and cultivation

rights on the land (Hunter et al. 1908). These records were then used by

the British for assessment of land revenue.

The land revenue records were presumed to be accurate reflections

of land rights, and although land revenue has been abolished since inde-

pendence from British rule, the system of maintenance of land records has

persisted. Several levels of administration are involved in the land record

maintenance; the District, Subdistrict, Kanungo circle, and Patwar circle.

At the lowest administrative level of the records system – the Patwar Cir-

cle – are patwaris. They are responsible for land record issues as well as

many social, political, and administrative tasks including keeping weather

9

records, collecting crop harvest information, reporting of village crimes,

and updating registers of voters. In Punjab, about 8,000 Patwaris main-

tain land records pertaining to 20 million land owners. The patwaris keep

their records in a cloth bag called a ‘Basta’. They are the custodians of

records pertaining to private as well as government lands. The transfer

of land is initiated at the level of the patwari, and affected at upper ad-

ministrative levels. Under present land legislation, there is no formal state

certificate or title to land; transfers, sales and attestation of land rights are

conducted based on the local officers’ records and arbitration. The local

officers’ act as middle-men in land market transactions and their responsi-

bility over record keeping yields considerable influence to them. In 2010,

the Government of Punjab in India made attempts to abolish colonial posts

like patwaris and kanungos, noting that these officers at the lower rung in

state’s revenue department were often accused of corruption and making

‘fraudulent changes’ in the revenue records of the area in which they have

jurisdiction (Sural 2013).

As areas become urbanized the land rights continue to be maintained

by patwaris until an agency or urban development authority acquires the

land. The urban land record system is similarly opaque. Overall there is

no single agency maintaining updated urban land records for all of Punjab,

and there is limited coordination in record keeping functions being carried

out by the various agencies. The ambiguity of law regarding records of land

rights is particularly harmful to the poor, who cannot afford protracted land

disputes. Numerous legal disputes are caused by contract enforcement of

land rental contracts, e.g. over illegal possession of land, eviction of tenants,

and recovery of rent. Cases of land disputes are either heard in the Revenue

10

Courts or Civil Courts, but as mentioned above, a lack of decisive land

records results in ambiguity and vagueness.

Punjab has a total area of 205,345 square kilometers, and is the most

populated province of Pakistan with 80.5 million inhabitants (55.6% of

Pakistan’s total population), 70% of who live in rural areas. Agriculture

plays an important role in the Province’s economy. However, the over-

all dispersed and duplicative nature of its land records makes land rights

uncertain, negatively impacts economic development, and threatens the

vulnerable and the poor whose rights remain virtually unprotected. Begin-

ning in years 2005-2009, the Government of the Punjab received financial

support from the World Bank to begin computerisation of Land Records

with the objectives of improving service delivery and enhancing the per-

ceived level of tenure security. The main objective of this endeavor was

to facilitate increased access to land records at low costs, specifically for

the poorest and less connected households. The provincial government

department noted that:

Inequalities of land distribution, tenure insecurity and difficul-ties associated with the land administration and registrationsystem are closely interrelated and continue to impose signifi-cant constraints on both rural and urban populations, particu-larly the poor. Land transactions are relatively high cost, anddisputes about accuracy of land rights are caused, among oth-ers, by the inefficient and dispersed land records system. Asa result land markets are thin and land prices are in excessof the discounted value of potential agricultural earnings fromland. The low mobility of land contributes to perpetuating thehighly unequal distribution of land and, thus, livelihood oppor-tunities. (World Bank – Project Information Document 2005)

The first objective of the program is to computerize all land records

as currently maintained by the local level patwar officers—these include all

11

rural land records, as well as urban records that still fall under the agrarian

land record maintenance system. Initially all records were part of the same

system. As regions became urbanized, record maintenance continued to be

held under the same system until land went into strictly private hands (

e.g. as residential societies or commercial land), whereby ownership records

were maintained by private agencies. Thus the computerisation affected

all rural records, as well as urban records that were maintained within the

patwari system instead of being monitored by an independent agency. The

computerized records can be looked up on the world wide web, and can

also be obtained from designated service centers that were set up for each

sub-district of the province.

These service centers provide land record maintenance services and

facilitate all types of land transactions, including sales, transfers and in-

heritance. The right holders (owners or tenants) can visit a service centre

where the staff can search and verify their record using their national ID

number, providing the client with a copy of their record within minutes.

This service centre can provide owners with a government attested copy of

the record, allowing him/her to use that record in a court of law similar to

using a title. Any mutation, due to to sale, transfer or inheritance, is to be

registered at the same service centre. These services were initially provided

by the Patwari ; following the new system, a signature from patwari would

not be required for the registration of any transaction, as it previously

was.5 150 centers across the province now provide automated land records

services, reducing the average time required to complete transactions from

5The project had a one-year transition period where right holders could go to their des-ignated service centre or the patwari for these services. After the interim period, only thedesignated service centre could provide these services.

12

2 months to 45 minutes (Gonzales 2016).

The project thus resulted in two main changes to the pre-existing

system: i) Centralized record keeping, 2) Low cost and centralized land

transactions. By making the computerized land record centrally available

at sub-district level the new system lowered the influence of the local officers

and patwaris in both land record keeping and land transactions. Thus the

program has potential affects on both tenure security and the land market.

4 Empirical Strategy and Data

I exploit the staggered rollout of the program by using a difference in dif-

ference strategy. The program roll out began in 2009 when some service

centers were set up to facilitate the field work required for obtaining, ver-

ifying and computerizing land records. A database was created for each

subdistrict with the computerized village records—the service centers com-

menced their rights verification and land transaction services even if 100%

of land records in their jurisdiction had not been digitized. The program

proposed one service centre for each subdistrict that would be located in

the ‘tehsil’ or subdistrict capital. By 2015 all subdistricts have a func-

tioning service centre, but there are still several subdistricts where only a

fraction of village records have been computerized. I use two measures to

obtain program progress or intensity at district level: (i) the share of vil-

lages in a district whose land records have been verified and computerized

in district d by the beginning of year t, and (ii) the fraction of subdistricts

in a district d that have a functioning service centre by year t. I run the

following specification:

yidt = β0 + βiProgramIntensitydt + χidt + Πd + Tt + Πd × T + εidt

13

where yidt is an outcome for household i in district d and year t. Πd and

Tt are district and year fixed effects respectively, while χidt is a vector of

household level controls. To control for district specific trends, I include

an interaction of district fixed effects with a linear yearly trend. Standard

errors are clustered at district-year level. The district fixed effects account

of time-unvarying differences across districts, allowing me to estimate the

change in outcomes as the program intensity is increases in any specific

district. The major concern in this kind of an identification strategy would

be that districts, particularly those where the program was implemented

with higher intensity, experience differential trends even in the absence

of the program. I thus control for district specific trends to account for

differential trends in outcomes across the districts. Even if outcomes have

a different trend across districts, unless this trend is collinear with the

progress of the program in a specific district, we should be able to estimate

the effect of ProgramIntensitydt if there is one.

I focus on the co-efficient on ProgramIntensitydt measured by the

number of service centers in any district as a percentage of the maximum

the district can have. The maximum service centers in a district is the

number of subdistricts. There are 36 districts and 150 subdistricts in total.

While the program primarily focused on rural land records, many urban and

peri-urban properties are still maintained under rural land management;

hence I show the effect for both rural and urban areas. Moreover, since

the poorest and least politically connected members of the population were

most vulnerable to land tenure insecurity due to ambiguities in the agrarian

law, I also disaggregate the effect by income group. I interact the treatment

with indicators for rural and urban areas as well as with indicators for

14

income quartile of the household. As an additional robustness check I also

include fixed effects for region dummy interacted with year and income-

quartile dummies interacted with year. These do not change my results

and are available at request.

The household data is obtained from Pakistan Living Standard Mea-

surement (PLSM) surveys conducted every year across the country. The

PSLM surveys are conducted at district level and at provincial level re-

spectively at alternate years – I use ten PSLM survey rounds from 2004-05

to 2014-15.6 PSLM district level surveys are representative at district level

surveying 80,000 households and collect information on demographics, em-

ployment, access to public services and key social indicators. The provincial

level surveys, conducted in 2005-6, 2007-8, 2011-12 and 2013-14, collect the

same as well as expenditure and saving information from a smaller sam-

ple of approximately 18,000 households across the country. The provincial

level survey has a larger questionnaire and smaller sample, while the dis-

trict level survey is the opposite. Summary statistics are shown in Table

2. The program progress data is obtained from the Board of Revenue of

Punjab, outlining for each year and district the number of service centers

that were functioning and the proportion of villages whose records had

been successfully computerized.

5 Results

As mentioned above, I first show the results (Tables ??-8) for ProgramIntensitydt

measured by the number of service centers in any district as a percentage

of the maximum the district can have.

6There is no PSLM round in 2009-10.

15

One of the intended effects of the program was to reduce the trans-

action cost in the land market by removing the role of middlemen like

textitpatwaris and initiating a digitally guided process. I examine actual

land sales/purchases in the next table; first, I explore evidence for this by

looking at overall land inequality as provided by gini coefficients. I calculate

the gini for owned agricultural land for the entire district for a particular

year, as well as for each primary sampling unit (PSU) in any year (see Ta-

ble 3). The PSU is the smallest stratum used for sampling the households

for the survey—this is generally a village in rural areas. I find an overall

negative impact on the level of land inequality, as measured by the gini.

When I focus just on rural PSUs (or villages), I find a stronger and more

significant decrease. Thus village level inequality goes down, presumably

as more people are able to acquire new land or declare ownership rights to

their existing land.

I first examine the direct effect of the program on reported ownership

of agricultural land and other properties, and on agricultural participation.

I find (see Table 4) that in districts with greater progress of the program,

there is no significant effect on overall reported land ownership. There

are higher reported sales and purchases, indicating slightly more fluid land

market. These coefficients are not highly significant, but are moderate sized

and may be imprecisely estimated due to the limited number of observations

as land purchase and sale data is only collected in the pronvincial survey

rounds. Examining the heterogeneity in the program effect reveals that

while some households are likely to acquire agricultural land, others tend

to shift away from agriculture. The urban households seem to acquire land

and their outcomes are consistent with those for landowners—these are

16

likely agricultural households that reside in the outskirts of rural areas,

and have less acess to land and less secure property rights. As a result

of the program, these households are more likely to report agricultural

land ownership and also more likely to rent out agricultural land. At the

same time Table 5 shows that they increasingly practice owner cultivation

instead of contract cultivation, consistent with them having better access

to agricultural land.

As households’ perception of tenure security improves, households

can make unconstrained decisions in the land and labor markets. I ex-

amine the effects of formalizing property rights on labor market outcomes

in Table 6. The risk of expropriation requires the owners or tenants to

stay physically attached to their property. In the case of agriculture, this

requires household members to stay in the village and participate in agri-

culture even when they may get a higher wage in non-farm employment

or by migrating outside the village. Thus tenure insecurity can limit labor

mobility and impede rural-urban transformation—as Fields (2007) notes

in the case of Peru informal institutions frequently arise to compensate for

lack of formal property protection, including community policing as well as

individual homeowners guarding their land. Increased tenure security for

rural households will reduce the opportunity cost of non-farm employment,

allowing households to make unconstrained decisions about the allocation

of labor. Table 6 shows the effect of the program on households’ participa-

tion in agricultural activities — HH Any Ag Work is an indicator if at least

one member of the household participates in agriculture as their primary

of secondary occupation, HH Income Share from Ag is the share of income

from agriculture. In the rural areas the program leads to households quit-

17

ting agriculture as shown by a significantly lower likelihood of participation

in agriculture as a primary or secondary occupation and a significant fall

in share of income from agriculture. A rural household is 3 percentage

points less likely to be involved in agriculture on average as a result of

the program; with a 46% participation rate overall, this is a 7% lower rate

of participation in agriculture. In turn households with businesses that

employ 10 or more people increases. Household’s income share from agri-

culture falls and so does the number of household members who who work

as unpaid family workers on the farm. These effects are most significant

for lower-middle income households in the rural areas. This movement out

of agriculture explains why we do not find large effects on reported land

ownership by rural households in Table 4.

Migration is another essential measure of labor market mobility. The

LSM surveys do not measure migration explicitly, but I have a measure

of remittance income that the household received from within or outside

the country. Presumably, these remittances are payments from migrant

household members who work in other parts of the country or outside

the country.7 I include these measures of remittance income as outcome

measures to proxy for the presence of a migrant household member. I do

find that remittances are significantly higher with the progress of the pro-

gram, indicating the program enhanced labor mobility by allowing resident

household members to migrate or existing migrants to migrate for longer.

Improved security of tenure and improved allocation of labor should

both affect input intensity and farm yields. In Table 7, the outcome vari-

ables are farm yield (total output value per acre), total input cost per acre

7The is a large diaspora of Pakistani unskilled migrant workers in the Middle-eastern coun-tries.

18

and input cost per acre for some key inputs (all measured in Rs/acre). Farm

yield data from urban households again shows that there are households

residing in an urban locations who participate in rural markets—this may

be because their location is just outside a rural area, or because locations

classified as urban in the LSM survey constitute areas with agricultural

land. There is significant increase in over all output and input intensity.

When I examine the specific input types, I find that households are more

likely to hire labor on their farm, a role which may have been initially

fulfilled by unpaid family labor. Households are also more likely to spend

on productive inputs like seeds and pesticide. These effects are however

driven by the upper income households, implying better input access for

them. The high incentive to invest may not materialize for lower income

households due to lack of input access for them.

In line with Field (2003), Do and Iyer (2008) and Giliani and Schar-

grodsky (2010), I do not find evidence supporting the De Soto effect. In

fact, as apparent in Table 8, the measures for credit access, as given by total

loan outstanding and amount borrowed last year are lower as the program

progresses. Since the amount borrowed from informal and formal sources

cannot be distinguished, we cannot be entirely sure that this result implies

reduced access to credit. Households may well utilize informal borrowing

to smooth consumption, in which case improved income and remittances

are consistent with loans going down as a result of the program.

6 Concluding Remarks

Tenure insecurity and thin land markets are a feature of rural Punjab (Pak-

istan) where agrarian law is informally defined for centuries and local of-

19

ficers have discretionary powers in land market transactions. This limits

mobility in the land and labor markets suppressing productivity, especially

for the poorest and least connected members of the population. Through

the Punjab government’s efforts land records have been digitized and are

available on the world wide web as well as physically at designated service

centers. Innovative use of ICT allowed land transactions to be automated

and free from involvement of corrupt officers and administrative hurdles.

The paper offers evidence that despite being very recent, program has man-

aged to significantly affect tenure security, investment and labor allocation

decisions of households, especially for rural and poor households. Moreover,

the land market responds positively and land inequality, which has been

persistently high in the Pakistani context, is lower due to the program.

The formalization of property rights can thus have potentially large

positive effects while obviating the financial and feasibility hurdles of titling

programs. The results also illustrate that land and labor market constraints

limit rural mobility in the South Asian context, shedding light on the rural-

urban divide and the prospect of structural transformation. Lastly, the

paper further reinforces our understanding of development economics by

exhibiting how ICT use is manifested in public service processes and can

ease market frictions in lower income countries.

20

7 Figures and Tables

Figure 1: A Cadastral Map for a village in Punjab (Adeel 2010)

Figure 2: A Land Record Register as maintained by Patwari (Adeel 2010)

21

A: Household Owns Agland8

-.50

.51

1.5

Coe

ffici

ent

-6 -5 -4 -3 -2 -1 0 1 2 3 4Years Since Program Start

Rural

-1-.5

0.5

Coe

ffici

ent


Urban

B: Household Income Share from Agriculture

-.50

.51

1.5

Coe

ffici

ent


Rural

-.6-.4

-.20

.2.4

Coe

ffici

ent


Urban

8Each chart plots the coefficients on dummy variables for numbers of years since program started in any district. The householdlevel regression includes district and year fixed effects, district specific linear trends and household controls. The left panel is aregression for rural areas, while the right panel is the same regression for urban areas

22

C: Household practices Agriculture9

-1-.5

0.5

1C

oeffi

cien

t


Rural

-1-.5

0.5

Coe

ffici

ent


Urban

D: Household practices Self-cultivation

-.50

.51

Coe

ffici

ent


Rural

-.6-.4

-.20

.2C

oeffi

cien

t


Urban

9Each chart plots the coefficients on dummy variables for numbers of years since program started in any district. The householdlevel regression includes district and year fixed effects, district specific linear trends and household controls. The left panel is aregression for rural areas, while the right panel is the same regression for urban areas

23

Table 1: Program Progress across the districts of Punjab Province

N mean sd2010% of Subdistricts with a Center 36 0.00694 0.0417% of Villages with Fully Functional Database 36 0 0

2011% of Subdistricts with a Center 36 0.0551 0.187% of Villages with Fully Functional Database 36 0 0

2012% of Subdistricts with a Center 36 0.164 0.351% of Villages with Fully Functional Database 36 0.0622 0.158



24

Table 2: Summary Statistics from Living Standards Measurement Survey Data

N mean sd N mean sd# of HH members 123,769 6.264 2.879 71,387 6.206 2.804# of HH members working 123,764 1.980 1.439 71,385 1.682 1.179# of HH members with primary occupation in Ag 123,764 0.905 1.318 71,385 0.0910 0.417# of HH members with secondary occupation in Ag 82,679 0.130 0.396 43,433 0.0167 0.140Head is literate 123,769 0.484 0.520 71,387 0.709 0.474HH total income (Rs/year) 123,769 163,475 286,851 71,387 229,058 453,003HH total agricultural income (Rs/year) 123,764 70,081 238,399 71,385 14,078 164,305HH net savings (Rs) 11,233 35,464 180,403 8,030 83,956 617,262Loans Outstanding (Rs) 11,233 40,534 486,681 8,030 40,452 270,347HH owns residential land 123,755 0.914 0.281 71,361 0.791 0.406HH owns non-agricultural land 123,755 0.0350 0.184 71,362 0.0370 0.189HH owns agricultural land 123,754 0.440 0.496 71,363 0.103 0.303HH rents out agricultural land 54,329 0.173 0.378 7,322 0.497 0.500HH rents in agricultural land 114,246 0.138 0.345 60,770 0.0183 0.134Remittances from within Country (Rs) 63,704 17,234 79,579 30,580 11,433 60,677Remittances from Abroad (Rs) 63,603 14,825 91,603 30,515 18,932 106,618Received Remittance from abroad or within Pakistan 63,706 0.215 0.411 30,583 0.162 0.369Farm Yield (Rs/acre) 4,416 60,090 57,136 343 63,557 43,906Total Input Cost (Rs/acre) 4,399 24,318 21,628 345 27,165 21,628Seed Cost (Rs/acre) 4,400 3,200 3,872 345 3,317 3,893Fertilizer Cost (Rs/acre) 4,400 6,297 6,010 345 7,218 7,273Pesticide Cost (Rs/acre) 4,400 2,107 2,730 345 2,487 2,693Permenant Labor Cost (Rs/acre) 4,399 420.4 2,432 345 1,789 4,384Casual Labor Cost (Rs/acre) 4,400 2,127 3,462 345 2,710 3,589

Rural Households Urban Households

Notes: Observation count may differ for some variables that were only reported for a subset of the survey rounds

25

Table 3: Program Effect on Agricultural Land Inequality

(1) (2) (3) (4) (5) (6)Gini Gini Gini Gini Gini Gini

District District PSU PSU PSU PSUProgram Intensity (% of Villages Computerized) -0.00470 -0.0229 -0.0596***

(0.0185) (0.0195) (0.0221)Program Intensity (% of Subdistricts with Centre) 0.00629 -0.0118 -0.0459**

(0.00836) (0.0109) (0.0214)Constant -1.952 -0.957 4.830 4.903 7.904 6.404

(2.652) (2.639) (6.251) (6.259) (5.040) (5.533)

Observations 351 351 13,983 13,983 7,836 7,836R-squared 0.773 0.773 0.174 0.174 0.041 0.041Dependant Variable Mean 0.835 0.835 0.599 0.599 0.701 0.701Sample Used for Regression All Districts All Districts All PSUs All PSUs Rural PSUs Rural PSUsNotes: Regressions (1) and (2) are at district level, with the district level gini coefficient for owned agricultural land as the dependant variable. Regressions (3)-(6) are at PSU level. Each PSU is a primary sampling unit for the survey, a geographical cluster that comprises about the size of a village. All regressions include district and year fixed effects, and control for district specificlinear trends. Standard errors are clustered at district-year level.

26

Table 4: Program Effect on Reported Agricultural Land Ownership

(1) (2) (3) (4)

Own (Y/N) Size (acres)Sold Agland in Last

YearPurchased Agland in

Last Year Panel A: Overall EffectProgram Intensity 0.0106 0.125 0.00115 0.00838*

(0.00962) (0.195) (0.00538) (0.00454)Constant 7.959*** -107.5 0.423 0.958

(3.003) (104.5) (1.375) (2.730)

Observations 195,117 61,729 7,413 7,413R-squared 0.168 0.118 0.015 0.017Mean of Dependant Variable 0.316 5.604 0.0108 0.00432

Panel B: Effect by Rural-Urban Location Program Intensity x Rural 0.00381 0.159 0.00212 0.00820*

(0.0115) (0.196) (0.00561) (0.00462)Program Intensity x Urban 0.0192 -0.0867 -0.00520 0.00956

(0.0119) (0.369) (0.0111) (0.00706)Constant 7.797*** -106.3 0.392 0.964

(2.991) (103.7) (1.382) (2.729)

Observations 195,117 61,729 7,413 7,413R-squared 0.168 0.118 0.015 0.017p-value of difference between rural and urban coefficient 0.248 0.471 0.519 0.828Mean of Dependant Variable: Rural 0.440 5.278 0.0100 0.00424Mean of Dependant Variable: Urban 0.103 8.031 0.0154 0.00481

Panel C: Effect by Income Quartile Rural Inc Q1 0.0183 0.423* 0.0136 0.00744*

(0.0149) (0.239) (0.0135) (0.00448) Inc Q2 -0.0222* 0.102 0.00889 0.00936*

(0.0131) (0.206) (0.0123) (0.00477) Inc Q3 0.000136 -0.0166 -0.0110 0.00885

(0.0149) (0.214) (0.00775) (0.00554) Inc Q4 -0.00636 -0.222 -0.00117 0.00617

(0.0184) (0.261) (0.00974) (0.00673) Urban Inc Q1 0.0311** 0.865 0.0229 0.0111**

(0.0148) (0.624) (0.0434) (0.00547) Inc Q2 0.0221 -0.200 -0.0510* -0.000419

(0.0179) (0.802) (0.0272) (0.0105) Inc Q3 0.0281** 0.446 0.00878 0.0116**

(0.0134) (0.431) (0.0363) (0.00499) Inc Q4 0.0231* -0.109 -0.00809 0.00991

(0.0130) (0.502) (0.00648) (0.0108)Constant 4.136 -172.3 0.270 0.852

(4.321) (139.3) (1.399) (2.763)

Observations 195,117 61,729 7,413 7,413R-squared 0.194 0.152 0.019 0.018Notes: The Program Intensity is measured as number of subdistricts in a district with a functioning land records service center. The regressions are at household level, with fixed effects for year, district and rural/urban locations, district specific linear trends and household level controls including household size, income quartile, gender and education of household head. Standard errors are clustered at district year level. Inc Q1, Inc Q2, Inc Q3 and Inc Q4 are dimmies indicating housheolds' income quartiles. Regressions for Panel C control for income quartile dummies and these dummies interacted with region dummies (rural/urban).

27

Table 5: Program Effect on Agricultural Tenancy Market

(1) (2) (3) (4) (5)

Ag land Rent Out (Y/N)

Ag land Rent In (Y/N) Self-Cultivation Sharecropping

Contract Cultivation

Panel A: Overall EffectProgram Intensity 0.0171 0.00589 0.00817 0.000679 -0.00749**

(0.0147) (0.00595) (0.00823) (0.00269) (0.00331)Constant 3.951 9.734** 0.760 -1.723 6.274

(12.28) (3.999) (5.183) (1.791) (4.565)

Observations 61,651 175,016 195,149 195,149 195,149R-squared 0.135 0.078 0.125 0.026 0.030Mean of Dependant Variable 0.211 0.0962 0.183 0.0186 0.0341

Panel B: Effect by Rural-Urban Location Program Intensity x Rural 0.0111 0.00464 0.00108 -0.00168 -0.00601

(0.0155) (0.00709) (0.00924) (0.00310) (0.00399)Program Intensity x Urban 0.0538** 0.00751 0.0171* 0.00367 -0.00937**

(0.0249) (0.00741) (0.00972) (0.00304) (0.00394)Constant 3.738 9.693** 0.591 -1.779 6.310

(12.16) (3.981) (5.275) (1.827) (4.595)

Observations 61,651 175,016 195,149 195,149 195,149R-squared 0.135 0.078 0.125 0.026 0.030p-value of difference between rural and urban coefficient 0.0851 0.732 0.103 0.0810 0.441Mean of Dependant Variable: Rural 0.173 0.138 0.271 0.0278 0.0495Mean of Dependant Variable: Urban 0.497 0.0183 0.0295 0.00276 0.00724

Panel B: Effect by Income Quartile Rural Inc Q1 0.0374* 0.00841 0.0259** -0.000858 -0.00326

(0.0223) (0.00808) (0.0121) (0.00445) (0.00464) Inc Q2 0.0203 -0.00777 -0.0278** -0.00664* -0.00877

(0.0167) (0.00877) (0.0114) (0.00374) (0.00629) Inc Q3 0.00193 0.00172 -0.000396 0.00347 -0.00700

(0.0168) (0.00912) (0.0129) (0.00389) (0.00494) Inc Q4 -0.00271 0.00812 -0.0242 -0.00139 -0.00936

(0.0175) (0.0129) (0.0182) (0.00355) (0.00615) Urban Inc Q1 0.0971** 0.0147* 0.0275** 0.00831** -0.00732*

(0.0405) (0.00889) (0.0132) (0.00363) (0.00421) Inc Q2 0.111** 0.00621 0.0181 0.000862 -0.00941**

(0.0555) (0.00841) (0.0130) (0.00352) (0.00463) Inc Q3 0.0371 0.0109 0.0244** 0.00469 -0.00815**

(0.0362) (0.00791) (0.0107) (0.00322) (0.00402) Inc Q4 0.0233 0.00884 0.0211** 0.00242 -0.00950**

(0.0335) (0.00775) (0.00981) (0.00298) (0.00421)Constant 4.304 8.295** -2.919 -1.741 5.822

(12.80) (3.356) (6.883) (1.828) (4.406)

Observations 61,651 175,016 195,149 195,149 195,149R-squared 0.147 0.086 0.159 0.027 0.033Notes: The Program Intensity is measured as number of subdistricts in a district with a functioning land records service center. The regressions are at household level, with fixed effects for year, district and rural/urban locations, district specific linear trends and household level controls including household size, income quartile, gender and education of household head. Standard errors are clustered at district year level. Inc Q1, Inc Q2, Inc Q3 and Inc Q4 are dimmies indicating housheolds' income quartiles. Regressions for Panel C control for income quartile dummies and these dummies interacted with region dummies (rural/urban).

28

Table 6: Program Effect on Household Allocation of Labor

(1) (2) (3) (4) (5) (6) (7) (8)

Any Member Ag Work Y/N)

HH Income Share from Ag

(%)

Perc of HH memebers in

Ag (%)

HH has a business (<10 employees)

HH has a business (10

or more employees)

Perc of HH members

unpaid family ag. workers

(%)

Total Remittances

Received (RS)

Remitance Received from

Outside Country (Y/N)

Panel A: Overall EffectProgram Intensity -0.0149 0.000979 -0.00270 -0.000837 0.000529** 0.00141 9,124*** 0.00911*

(0.0106) (0.00822) (0.00484) (0.000583) (0.000249) (0.00338) (2,369) (0.00490)Constant 8.054 5.280 6.847** 0.0400 0.208** 9.451*** -687,974 1.140

(7.778) (6.821) (3.448) (0.319) (0.0930) (1.812) (1.964e+06) (3.294)Mean of Dependant Variable 0.314 0.239 0.101 0.00299 0.000545 0.0415 31479 0.0656

Panel B: Effect by Rural-Urban Location Program Intensity x Rural -0.0310** -0.0177* -0.00934 -0.00121* 0.000506** -0.00172 9,916*** 0.00801

(0.0129) (0.0101) (0.00603) (0.000674) (0.000232) (0.00416) (2,564) (0.00519)Program Intensity x Urban 0.00546 0.0246** 0.00571 -0.000338 0.000559* 0.00538 7,851*** 0.0109*

(0.0133) (0.0109) (0.00629) (0.000871) (0.000322) (0.00440) (2,843) (0.00641)Constant 7.672 4.837 6.689** 0.0312 0.208** 9.377*** -698,581 1.155

(7.584) (6.603) (3.365) (0.327) (0.0930) (1.773) (1.977e+06) (3.272)p-value of difference between rural and urban coefficient 0.0256 0.00124 0.0556 0.345 0.820 0.192 0.412 0.632Mean of Dependant Variable: Rural 0.460 0.352 0.151 0.00154 0.000257 0.0631 32035 0.0634Mean of Dependant Variable: Urban 0.0619 0.0442 0.0152 0.00574 0.00109 0.00405 30322 0.0701

Panel C: Effect by Income Quartile Rural Inc Q1 -0.0185 0.0128 -0.00555 -0.000895 0.000528** -0.000614 22,086*** 0.0165

(0.0160) (0.0153) (0.00661) (0.000641) (0.000254) (0.00405) (4,540) (0.0104) Inc Q2 -0.0640*** -0.0602*** -0.0252*** -0.000844 0.000508** -0.00915** 5,312* 0.00623

(0.0157) (0.0132) (0.00705) (0.000648) (0.000233) (0.00461) (3,199) (0.00606) Inc Q3 -0.0131 -0.00441 -0.00304 -0.00150** 0.000683*** -0.000276 7,597** 0.00844

(0.0156) (0.0123) (0.00702) (0.000696) (0.000235) (0.00502) (3,270) (0.00620) Inc Q4 -0.0534** -0.0403* -0.00886 -0.00172* 0.000549** -0.000570 5,829 0.00262

(0.0209) (0.0218) (0.0101) (0.00101) (0.000256) (0.00781) (3,750) (0.00672) Urban Inc Q1 0.0217 0.0375*** 0.0100 -0.00164** 0.000600** 0.00775 26,059*** 0.00961

(0.0165) (0.0128) (0.00729) (0.000773) (0.000277) (0.00476) (9,398) (0.0184) Inc Q2 0.00211 0.0220* 0.00561 -0.000342 0.000417 0.00478 3,484 0.0121

(0.0169) (0.0132) (0.00717) (0.000663) (0.000289) (0.00469) (3,499) (0.00877) Inc Q3 0.0158 0.0314*** 0.00899 1.61e-05 0.000866** 0.00676 4.240 0.00687

(0.0146) (0.0116) (0.00643) (0.00102) (0.000358) (0.00432) (3,788) (0.00882) Inc Q4 0.00704 0.0270** 0.00736 0.000389 0.000536 0.00639 5,690 0.0103

(0.0128) (0.0116) (0.00606) (0.00179) (0.000637) (0.00439) (4,698) (0.00928)Constant 4.348 2.275 5.521* -0.0427 0.206*** 8.762*** -189,716 2.020

(7.186) (6.170) (3.199) (0.346) (0.0786) (1.548) (2.391e+06) (3.600)

Observations 195,156 195,149 195,149 126,112 126,112 195,149 94,289 94,232R-squared 0.247 0.204 0.193 0.023 0.008 0.131 0.113 0.127Notes: The Program Intensity is measured as number of subdistricts in a district with a functioning land records service center. The regressions are at household level, with fixed effects for year, district and rural/urban locations, district specific linear trends, and household level controls including household size, income quartile, gender and education of household head. Standard errors are clustered at district year level. Inc Q1, Inc Q2, Inc Q3 and Inc Q4 are dimmies indicating housheolds' income quartiles. Regressions for Panel C control for income quartile dummies and these dummies interacted with region dummies (rural/urban).

29

Table 7: Program Effect on Farm Investment

(1) (2) (3) (4) (5) (6)

(Rs/acre) YieldTotal Input

CostPermanent

Hired LaborCasual Labor Pesticide Seeds

Panel A: Overall EffectProgram Intensity 13,070* 4,568* 325.1 1,731*** 943.9*** 326.7

(7,263) (2,456) (225.0) (606.0) (305.1) (635.5)Constant 6.002e+06 1.988e+06 -80,272 258,081 468,244*** 242,131*

(3.743e+06) (1.216e+06) (57,485) (511,807) (68,283) (129,981)

Observations 4,759 4,744 4,744 4,745 4,745 4,745R-squared 0.268 0.356 0.046 0.240 0.332 0.220Mean of Dependant Variable 60340 24525 519.9 2169 2134 3208

Panel B: Effect by Rural-Urban Location Program Intensity x Rural 12,766* 4,362* 237.3 1,639*** 961.9*** 297.3

(7,328) (2,502) (234.8) (606.1) (307.3) (641.6)Program Intensity x Urban 16,651* 6,988* 1,355 2,810*** 733.2* 671.6

(8,696) (4,076) (1,154) (822.6) (397.2) (766.6)Constant 6.012e+06 1.995e+06 -76,882 261,654 467,547*** 243,273*

(3.738e+06) (1.212e+06) (58,219) (510,476) (68,611) (129,860)

Observations 4,759 4,744 4,744 4,745 4,745 4,745R-squared 0.268 0.356 0.047 0.241 0.332 0.220p-value of difference between rural and urban coefficient 0.530 0.487 0.357 0.0555 0.414 0.484Mean of Dependant Variable: Rural 60090 24318 420.4 2127 2107 3200Mean of Dependant Variable: Urban 63557 27165 1789 2710 2487 3317

Panel C: Effect by Income Quartile Rural Inc Q1 11,964 2,785 -520.4* 878.4 874.6 -26.37

(9,969) (2,716) (266.3) (655.7) (536.1) (673.5) Inc Q2 7,151 2,017 -343.5 890.0 139.8 91.68

(8,207) (2,681) (271.2) (647.4) (533.2) (647.4) Inc Q3 14,243* 3,888 -83.80 1,558** 595.0 568.4

(8,381) (2,717) (253.9) (654.3) (510.6) (710.5) Inc Q4 15,926** 7,022** 1,105*** 2,377*** 99.73 313.0

(7,919) (2,705) (334.8) (650.1) (546.7) (630.9) Urban Inc Q1 25,441 6,586 -1,092** 892.2 2,413 5,969

(27,981) (7,037) (427.3) (1,235) (3,296) (7,876) Inc Q2 3,468 744.2 1,968 253.3 482.2 1,069

(13,465) (6,606) (3,431) (985.4) (1,192) (1,162) Inc Q3 22,443* 5,247 1,043 1,439 431.9 807.7

(12,504) (5,068) (1,674) (893.4) (1,109) (973.8) Inc Q4 17,409* 9,615** 1,518 4,079*** 39.16 117.8

(8,792) (4,391) (973.4) (1,016) (821.3) (755.0)Constant 4.965e+06 1.730e+06 -32,229 246,817 208,226 198,705

(3.680e+06) (1.218e+06) (51,339) (513,730) (165,793) (129,427)

Observations 4,759 4,744 4,744 4,748 4,745 4,745R-squared 0.271 0.357 0.073 0.077 0.306 0.037Notes: The Program Intensity is measured as number of subdistricts in a district with a functioning land records service center. The regressions are at household level, withfixed effects for year, district and rural/urban locations, district specific linear trends and household level controls including household size, income quartile, gender and education of household head. Standard errors are clustered at district year level. Inc Q1, Inc Q2, Inc Q3 and Inc Q4 are dimmies indicating housheolds' income quartiles. Regressions for Panel C control for income quartile dummies and these dummies interacted with region dummies (rural/urban).

30

Table 8: Program Effect on Household Savings and Credit

(1) (2) (3) (4) (5)

HH Net SavingHH Net Saving

Last Year Saving RateOutstanding

LoanAmt Borrowed

Last YearPanel A: Overall EffectProgram Intensity -12,298 -9,866 -0.120 -18,543 -5,433

(21,663) (12,610) (0.0811) (17,218) (4,425)Constant 1.442e+06 -1.504e+06 5.974 -2.071e+06 -1.735e+06**

(4.202e+06) (2.622e+06) (15.72) (4.439e+06) (768,727)

Observations 19,263 19,263 17,944 19,263 19,263R-squared 0.110 0.136 0.010 0.098 0.032Mean of Dependant Variable 55678 21196 0.176 40500 17742

Panel B: Effect by Rural-Urban Location Program Intensity x Rural 6,871 -5,643 -0.0708 -18,072 -2,593

(22,735) (12,834) (0.0864) (18,061) (4,178)Program Intensity x Urban -43,324 -16,701 -0.198* -19,305 -10,030

(26,658) (14,367) (0.106) (21,267) (6,336)Constant 554,988 -1.699e+06 3.818 -2.093e+06 -1.867e+06**

(4.458e+06) (2.698e+06) (16.25) (4.545e+06) (789,529)

Observations 19,263 19,263 17,944 19,263 19,263R-squared 0.111 0.136 0.010 0.098 0.032p-value of difference between rural and urban coefficient 0.0262 0.272 0.158 0.946 0.148Mean of Dependant Variable: Rural 35464 14838 0.163 40534 18807Mean of Dependant Variable: Urban 83956 30091 0.193 40452 16252

Panel C: Effect by Income Quartile Rural Inc Q1 35,949 488.4 0.304 36,919 6,925

(24,842) (11,257) (0.341) (45,177) (6,949) Inc Q2 3,570 -6,489 -0.152 7,795 -7,105

(22,062) (11,701) (0.105) (29,652) (5,263) Inc Q3 1,945 -12,238 -0.119 10,351 -3,116

(21,692) (10,915) (0.110) (22,236) (5,084) Inc Q4 -17,675 -4,576 -0.203* -124,084 -8,967

(55,773) (32,015) (0.110) (81,113) (7,231) Urban Inc Q1 1,231 -8,814 -0.579 41,355 1,485

(41,111) (16,741) (0.523) (52,954) (8,923) Inc Q2 31,947 4,713 -0.124 27,295 -6,520

(28,581) (14,405) (0.124) (35,804) (8,620) Inc Q3 1,556 -6,619 -0.141 -12,022 -8,956*

(26,332) (13,083) (0.112) (25,639) (5,260) Inc Q4 -123,878*** -35,831 -0.204* -73,964* -16,805

(44,411) (23,562) (0.109) (40,826) (11,000)Constant 479,409 -1.644e+06 5.752 -1.110e+06 -1.917e+06**

(4.369e+06) (2.558e+06) (18.08) (5.307e+06) (800,617)

Observations 19,263 19,263 17,944 19,263 19,263R-squared 0.114 0.139 0.016 0.111 0.033Notes: The Program Intensity is measured as number of subdistricts in a district with a functioning land records service center. The regressions are at household level, with fixed effects for year, district and rural/urban locations, district specific linear trends and household level controls including household size, income quartile, gender and education of household head. Standard errors are clustered at district year level. Inc Q1, Inc Q2, Inc Q3 and Inc Q4 are dimmies indicating housheolds' income quartiles. Regressions for Panel C control for income quartile dummies and these dummies interacted with region dummies (rural/urban).

31

computerization and development: formalizing property rights … · 2018-02-26 · computerization...

Documents