Discussion Paper No. 67

Land Property Rights and Agricultural
Productivity: Evidence from Panama
Gabriel Ivan Fuentes Cordoba

March, 2017

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANLand Property Rights and Agricultural
Productivity: Evidence from Panama∗
Gabriel Ivan Fuentes Cordoba†

Abstract
This study estimates the effects of land property rights on agricultural productivity in Panama. By using district-level panel data from1990 to 2010, I find that land privatization increases rice yield in agricultural labor-intensive districts, but does not have any significant
impact in non-agricultural labor-intensive districts. Then, by using
household-level data, I find that households with registered land titles
are more likely to obtain an agricultural loan and undertake landattached and land mobile investment.
(JEL O13, O15)
Keywords: Property rights, agricultural productivity, Panama, panel
data, investment

1

Introduction

Land property rights are key to improving the use of land resources, encouraging higher levels of investment and credit, and maximizing allocative
efficiency. Empirical evidence suggests that land tenure affects productivity
through three main channels: investment, borrowing, and land transfer. The
idea that tenure security can positively affect agricultural output is hardly
controversial. Previous studies dealing with the effects of land property rights
have offered insight into how secure land rights can affect the agriculture sector.1∗

Acknowledgments: I am thankful to Kentaro Nakajima for his guidance and advices.
I am also grateful to the comments from Shinn-Shyr Wang and all the participants at the
3rd Annual Workshop in Business and Economics in National Chengchi University, Taipei
2016. All remaining errors are mine.
†
Tohoku University, Graduate School of Economics and Management, Address: 27-1
Kawauchi, Aoba-ku, Sendai, Miyagi 980-8576 JAPAN, gfuentescordoba@gmail.com
1
See Deininger and Feder (2009) for a review of the impact of land registration on
investment and productivity.

1First, secure land rights provide incentives to farmers to invest and use
land productively, as farmers believe that investment in and the proper useof land give rise to significant benefits. Second, land can be used as collateralin the formal credit market, depending on the existence of a formal credit
market in the first place. Finally, land rights can be transferred, resulting
in a more efficient land allocation. Brasselle et al. (2002) refer to thesechannels as the “assurance,” “collateralizability,” and “realizability” effects,
suggesting that they are the key reasons why complete land rights should
encourage investing.
Formal land titling is a common way in which to ensure land tenurerights; however, in the majority of cases, this is expensive and difficult tocarry out. The most common way in which to insure land rights is throughland titling programs and land reforms, which have been introduced in almost every country in Latin America since the 1960s. Although land reforms
have failed to alleviate land inequality and address the lack of land titles
in rural households,2 national governments and international development
agencies extensively sponsor land titling programs to improve the use of landin developing countries. Atwood (1990) argues that in Africa there is sufficient evidence to counter the conventional view of land registration because
the cost of titling might be high and without the expected effects. However, more recent studies have found evidence that increasing land rights in
African countries may indeed improve land tenure security and land-related
investment (Fenske, 2010, 2011).
This study focuses on formal land property rights and their effect on
agricultural productivity in Panama. I analyze the impact of land title registration to explore the main channels through which property rights affectproductivity in the agriculture sector. As in other Latin American countries, agrarian reform and land certification programs have been common in
Panama since the 1960s. The first agrarian code was introduced by Law 37
of 1962 and it has since been modified multiple times. This code aims toincorporate people from rural areas into the economic growth process andincrease the efficiency of land through land titling. Certification programs
are also common in Panama; however, they tend to be highly bureaucratic
and without remarkable results. For example, in 1960, 27% of the land used
for agriculture purposes was completely titled, whereas in 2010, 49% of thisland was titled. In other words, after 50 years of intense struggle as well as
different regulations and land authorities, less than half of the land used for
agriculture remains improperly registered.
2

See De Janvry and Sadoulet (1989) for an explanation of the lack of success of land
reforms in Latin America.

2

The impact of land registration programs and land property rights on
agricultural productivity and investment incentives has been vastly researched.
According to Besley (1995), there are four reasons why complete rights should
affect economic activity: property rights strengthen claims to the fruits ofinvestment, improve access to capital, facilitate gains from trade, and reduce
unproductive costs. On agricultural productivity, for example, Chankrajang(2015), using partial land rights that cannot be transferred, finds that land
security affects labor productivity but has no effect on yields in Thailand.
The empirical findings on land rights and investment have yielded contradictory results. Fenske’s (2011) meta-analysis in Western Africa points outthat econometric techniques using binary investment measures and studies
that control for household-level heterogeneity are unlikely to find an impactof land rights on investment. However, the author finds a strong relationshipbetween land rights and fallowing. Similarly, Goldstein and Udry (2008),
using the social and political characteristics of farmers as instrumental variables, find that secure land tenure is associated with higher levels of investment in Ghana. In the case of Latin America, Deininger and Chamorro(2004) estimate the impact of land formalization on investment in Nicaragua.
Their findings suggest that properly registered land titles have a significant
investment-enhancing impact and that land title registration is associatedwith an increase in land value, which might have a positive distributional
effect. Land titling is also associated with higher levels of investment in Peru
(Fort, 2008).
Empirical evidence suggests that an improvement on land rights increases
access to credit in countries with formal credit markets. In Thailand, Federet al. (1988) find evidence that farmers with more secure land rights areable to borrow more than their counterparts. However, studies focused on
African countries could not find the same evidence; instead, Migot-Adholla
et al. (1991) find that farmers with individual land tenure did not have better
access to credit than those with other types of tenure systems. In Central
America, better access to credit does not necessarily follow land reforms. In
Honduras and Nicaragua, for example, the expansion of land titling reforms
did not lead to better access to formal credit. Smallholders have lower credit
market participation after titling reforms in Honduras, while in Nicaragua
rural financial markets are insufficiently evolved to improve credit access(Boucher et al., 2005).
Higher levels of land security should improve land markets by reallocating land to more efficient users. Boucher et al. (2005), using a descriptive
analysis, suggest that land titling reforms activate land rental markets. However, the authors also mention that land rental markets do not affect operated
farmland distribution in Central American countries. Another study focusing3

on Nicaragua found that large landowners are more likely to purchase land
and that this could be for unproductive reasons (Deininger et al., 2003).
Further, households without land titles are less likely to rent out land. In
other words, the lack of a title is a disincentive to participate in land market
activity because of the danger of losing land (Deininger et al., 2003).
The literature on land formalization and registration on investment, access to credit, and land transfer shows that the body of evidence is far fromconclusive and even contradictory in some cases. This research therefore aims
to contribute to the literature on land rights and agricultural productivity
with new empirical findings, exploiting two levels of regionally aggregateddata and household-level data. The main findings of this research are as
follows. First, land privatization increases rice yield per hectare in agricultural labor-intensive districts in Panama, but has no significant impact in
non-agricultural labor-intensive districts. Second, an increase in privately
owned land per district is not associated with a rise in labor productivity.
Lastly, households with land title deeds that are properly registered are more
likely to obtain an agricultural loan as well as undertake land-attached andland mobile investment. To the best of my knowledge, no similar study
has evaluated the impact of land registration on agricultural productivity inPanama.
The rest of the paper is organized as follows. Section 2 provides the
background. Section 3 describes the data sources, empirical strategy, anddescriptive statistics. Section 4 presents the results of land rights and agricultural productivity in Panama. Finally, Section 5 concludes the study.

2

Land reform and privatization movements

Small-scale farmers tend to suffer from land tenure insecurity in Latin America. To solve this issue, land reforms were introduced in a plethora of Latin
American countries including Panama. The majority of these reforms were
implemented at the beginning of the 1960s under the Inter-American Committee of Agricultural Development, which was created by the United States’
Alliance for Progress plan. In the case of Panama, the agrarian reform wasinstitutionalized by Law 37 of 1962. Law 37 of 1962, coupled with more
recent acts, aimed to incorporate farmers and people from rural areas ofPanama into the economic growth process, with a more equal distribution ofland, better access to agricultural credit, and technical assistance to improvetheir productivity and standards of living.
Law 37 of 1962 explained in detail the role of the Agrarian Reform Commission and encouraged peasants to register their land to increase tenure4

security and have easier access to credit. Although the reform was unsuccessful, five years after Law 37 came into force, there was growing demand forland certification. However, only a small number of the land titles requested
were granted. In fact, the certification and registration process was highlybureaucratic and it discouraged farmers from obtaining and formally registering their land titles. As in other Latin American countries, the agrarianreform failed to make the structural change needed to improve the agriculture sector, raise the living standards of farmers, and distribute land moreequally.
In Latin America, agricultural land distribution is unequal. Lipton (2009)
constructs a table of the Gini coefficients of operated farmland to measure
household-level farmland inequality for 49 developing countries. According
to this table, Panama’s farmland Gini coefficient in 1950 was 0.72, whereas
by 1990 it had increased to 0.87. In other words, farmland distribution
inequality increased after the land reform movements. Only Chile, Paraguay,
and Venezuela had larger farmland inequality according to Lipton’s table andPanama had the largest land inequality of all Central American countries.
Land reforms have had mixed results according to many studies. Recently, the focus changed towards land titling and certification programs,
sponsored by national governments and international development agencies.
The main goal of these types of programs is to foster agricultural productivity and provide land property rights to farmers. Panama has received loans
from international agencies to implement registration policies, and the percentage of agricultural land with registered titles drastically increased fromclose to 30% to almost 50% from 1990 to 2010. This rise in land privatization
occurred during a period when Panama was politically and economically stable. Furthermore, labor in the agriculture sector decreased sharply from 27%
in 1990 to close to 13% in 2010. However, in rural areas of Panama, agriculture remains the most important activity for the majority of households. In
fact, the agriculture sector is the second largest employer in the country and
it is the most important economic sector in a great number of districts inPanama. However, agricultural productivity is low, specifically seven times
lower than productivity in the other sectors in Panama (Hausmann et al.,2016).
Land legislation continues to change in Panama. In 2010, a new landauthority was established to administer and regulate land property rights.
ANATI (the National Authority of Land), among its many other functions, isin charge of mediating land-related disputes, dictating policies on indigenous
peoples’ land, and improving the cadastral system. The most updated changethat the Agrarian Code suffered was in 2011, regulating agrarian activities,
contracts, and sustainable land use. Hence, land regulations are constantly
5changing and they are still a polemic topic in Panama.

3

Data and empirical strategy

3.1

Data

The main dataset is taken from Panama’s Agricultural Census of 1990, 2000,
and 2010, compiled by the National Institute of Statistics and Census ofPanama (INEC), the governmental institution that collects, processes, andprepares Panamanian statistics. I also use data from the National Census
of Population and Housing of 1990, 2000, and 2010 collected by INEC. The
first Agricultural Census in Panama took place in 1950, and the most updated Agricultural Census was held in 2010. The data used in this study are
aggregated at the district and corregimiento levels. Panama is divided into
provinces, districts, and corregimientos. Figure 1 shows the administrative
political division of Panama at the corregimiento level.
The Agricultural Census offers aggregated data on the number of plots
and hectares by province, district, and corregimiento. There were some political changes at the district level during the study period. First, in 1997,
Comarca Ngobe Bugle, an indigenous peoples’ province, was created. Suchprovinces are called “Comarcas” in Panama. I omit Comarca districts andall districts that suffered political changes from the creation of the Comarcas
because of their special landholding system.3 Then, in 2001, the district ofMontijo was split into two districts. I added the results of the two districts
to use them in the panel data analysis.
From 1990 to 2010, Panama suffered a great amount of political change
at the corregimiento level. For this reason, only corregimiento-level data areused in the cross-sectional analysis in this research. In 2012, Panama wasdivided in 621 corregimientos. I drop all corregimientos that do not produce
rice and from the metropolitan areas of Colon and Panama.
This study uses the Living Standards Measurement Study (LSMS) to
analyze the mechanisms at the household level from 1997 and 2008. The
LSMS is a household survey organized by the World Bank and the Ministryof Economy and Finance in Panama. Three independent rounds have been
held in Panama, in 1997, 2003, and 2008. The data are pooled cross-sectional
data.
3

Comarcas are the indigenous areas in Panama. The main land tenancy system inthese areas is collective landholdings; therefore, they are not included in this research.

6

Figure 1: Townships in Panama

Note: Panama at the township level in 2010. Panama had 621 townships
according to the Statistics Bureau of Panama.

3.2

Empirical strategy for the baseline analysis

To investigate the impact of privately owned land on agricultural productivity, this study uses district-level panel data with fixed effects from 1990 to2010 in Panama.
To measure the impact of land property rights on agricultural output, thespecification is
ydt = αd + βt + γxdt + ϕzdt + εdt(1)where ydt is the log of agricultural yield. αd is the district fixed effect andβt represents the year fixed effect. xdt is complete land rights, defined as theshare of privately owned land to the total land used for agricultural activities
and zdt represents the time-variant control variables.
The main variable used to capture the effect of land property rights atthe district level is the share of privately owned land. I define this as theshare of land used for agricultural purposes by district where the ownerpossesses the title deed formally registered to the legal authority (Registro
Publico de Panama). I use two proxies for agricultural productivity: riceland yield and labor productivity. Rice yield is defined as rice harvested(in hundredweight) divided by the area of net rice sown (in hectares) per
7

district. Labor productivity is defined following Chankrajang (2015) as rice
harvested (in hundredweight) divided by the number of farmers by district.
Not all farmers are rice producers, but this variable is created under theassumption that the percentage of rice producers is constant over time.4
Rice is a staple grain in Panama and is produced in every district. No othercrop is cultivated in every district. Then, the share of privately owned landmeasures the impact of land certification and registration on agricultural
productivity.
This study’s empirical strategy could be argued to suffer from endogeneity
such as reverse causality. For example, districts that are poor, less productive, or with lower levels of investment may attract government attention
to implement land registration policies. However, these are, to some extent,
time-invariant characteristics; thus, using fixed effects at the district level
mitigates this concern. Another issue of the strategy adopted in this study isthat land registration may correlate with unobservable district-specific characteristics that also affect agricultural outcomes. I deal with this endogeneity
concern by including important district-specific control variables at the district level.
The specification might also suffer from within-district heterogeneity. To
solve this issue, I use cross-sectional data at the corregimiento level. Using
data from 2010 permits the inclusion of more control variables.5 The crosssectional corregimiento specification is as follows:yi = α + βxi + γzi + εi(2)where yi is the log of rice yield. As before, rice yield is defined as rice harvested (in hundredweight) divided by the area of net rice sown (in hectares)
per corregimiento. xi is complete land rights, defined as the share of privately
owned land to the total land used for agricultural activities and zi represents
the time- variant and -invariant control variables.
A more disaggregated level of data than the household level is arguably
preferred to study the impact of land certification and registration. Therefore, I use household-level data to explore some of the possible mechanisms
4

Chankrajang (2015) argues that rice cultivation requires a specific skill, fixed investment, and long-term preparation. Then, the share of rice farmers in a district could beconstant.
Using the logarithmic form and fixed effect,
riceproductionit
log ricef armersit = log(riceproductionit ) − log(ricef armersit )it= log(riceproductionit ) − log(φi f armersit )log riceproduction
 ricef armersit 
riceproductionit
log ricef armersit = log(riceproductionit ) − log(φi ) − log(f armersit )where log(φi ) is captured as the district fixed effects.
5
More data are available in 2010 than in 1990.

8

Table 1: Descriptive statistics
Mean
Standard deviation
Maximum
Minimum
Mean in:
1990
2000
2010
Mean
Standard deviation
Maximum
Minimum

Rice yield (cwt per ha)
36.11
23.72
103.34
5.65
32.71
35.86
39.74
Log of rice yield
3.38
0.64
4.64
1.73

Rice yield (cwt per ha)
32.46
34.26
420.46
0.12
Log of rice yield
Mean
3.16
Standard deviation
0.79
Maximum
6.04
Minimum-2.09
Mean
Standard deviation
Maximum
Minimum

District level 1990–2010
Share of privately owned land Total land (ha)0.4243063.13
0.2248624.38
0.87497746.6
0.01
384.53Farmers2646.522205.83
10519
103

0.32
46634.44
3121.160.38
43869.38
2505.840.5438685.57
2312.57
Pop. density
Illiteracy %Log of labor productivity
88.27
13.47
2.50
309.839.25
1.46
2771.7
50.4
6.32
2.4
1.6
-1.79
Corregimiento level 2010
Share of privately owned land Total land (ha)Farmers
0.54
4781.02375.900.26
5830.53352.530.9958430.342546
0112.17
14
Pop. density
Illiteracy %Rainfall
87.06
10.28
33406.55
268.04
6.73
1930.473734.2
33.26
33836
0.3
1.08
1280.4

Rice produced per farmer
34.67
67.69
554.34
0.17
23.61
39.32
41.07
Workers in agriculture %
45.1
22.96
83.99
1.25
Rice produced per farmer
50.38
172.352219.44
0.01
Log of labor productivity
1.96
1.94
7.70
-4.95

through which land registration affects agricultural productivity: loans, landattached and mobile investment, and low cost investment. I explain theempirical strategy at the household level in Section 4.2.
3.2.1

Descriptive statistics

Table 1 shows the summary statistics of the main variables at the district andcorregimiento levels. The upper part of Table 1 shows the summary statistics
for the district-level data from 1990 to 2010, while the lower part indicatesthe descriptive statistics at the corregimiento level in 2010. At the districtlevel, between 1990 and 2010, mean rice yield was 36.11 cwt per hectare. The
district with the highest productivity yielded 103.34 cwt per hectare, while
the least productive one yielded 5.65 cwt per hectare. From 1990 to 2010,
rice productivity steadily increased and so did the share of privately owned
land at the district level. The corregimiento-level data show similar patterns
to the district-level data; however, the dispersion of the results tends to behigher.

9

4

Results

4.1

Effects of land title registration on agricultural
productivity

This section presents the results of the empirical specification in equation (1)and equation (2). Table 2 shows the baseline results, using the district-level
panel data. The coefficient of the share of privately owned land in Column
1 indicates that an increase in land privatization raises rice land yield after including the year and district fixed effects that control for year shocks,
time-invariant geographical characteristics, and labor market opportunities
that might affect the results. The coefficient of privately owned land loses its
significance in Column 2 when I include important time-variant district characteristics as population density, illiteracy percentage, and median income.
The population density coefficient is positive but not statistically significant.
Illiteracy measures human capital and the coefficient is negative and statistically significant. Lastly, median income measures the income status of an
average household in a district and it does not have a significant effect inColumn 2.
In Column 3, I repeat the analysis in Column 2, but this time I restrict
the sample to agricultural labor-intensive districts. I divide the aggregateddata into two groups, agricultural labor-intensive and non-agricultural laborintensive districts. In the dataset, the median value of the share of workers in
the agriculture sector is around 45% and 42% per district and corregimiento,
respectively. For this reason, I define agricultural labor-intensive areas as
those areas where farmers represent 40% or more of the total working population. The coefficient of the share of privately owned land is positive andstatistically significant in Column 3, indicating that an increase in land privatization raises rice land yield in agricultural labor-intensive districts. In
Column 4, I use non-agricultural labor-intensive districts and the coefficientsare negative but not statistically significant. In sum, Panel A of Table 2
implies that agricultural labor-intensive districts with a higher share of complete land rights show a greater improvement in rice output per cultivatedarea.
Panel B of Table 2 shows the effects of land rights on labor productivity.
The coefficient of the share of privately owned land in Column 5 is positive,
but not statistically significant after including the fixed effects. In Column
6, I add the time-variant control variables to the fixed effects and again thecoefficient of the share of privately owned land is not statistically significant.
Similarly, in Column 7, the coefficient of the share of privately owned land
is not significant in agricultural labor-intensive districts. In Column 8, I
10

restrict the sample to non-agricultural labor-intensive districts and, similar toColumn 4, the coefficient is negative and not statistically significant. These
results indicate that there is not a statistically significant impact of land
privatization on labor productivity.

11

12

Whole dataset(1)0.401*(0.237)

A: Log rice yield
Whole dataset Farmers>40% Farmers<40%
(2)
(3)
(4)
Share of privately owned land
0.427
0.481*-0.539(0.277)(0.254)(0.585)Population density
0.00030.005
0.0001(0.0003)(0.021)(0.0004)
Log median income-0.154*-0.135-0.137(0.092)(0.106)(0.421)
Illiteracy percentage-0.021*-0.012-0.081(0.011)(0.012)(0.065)District FE
Yes
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Yes
Observations
171
171
101
70
Adjusted R-squared
0.144
0.01
0.077
0.07
Note: Robust standard errors are in parentheses.
***, **, and * stand for significance at 1%, 5%, and 10%. FE means fixed effects.
Yes
Yes
171
0.001

Whole dataset
(5)
0.193
(0.381)

B: Log labor productivity
Whole dataset Farmers>40%
(6)
(7)
0.08
0.265
(0.468)(0.393)-0.001***
0.004(0.0003)(0.034)-0.553***-0.457***(0.026)(0.17)-0.092***-0.063***(0.026)(0.019)
Yes
Yes
Yes
Yes
171
101
0.031
0.005

Table 2: Effects of land titling on log yield, 1990–2010
Farmers<40%(8)-0.539(0.585)0.000(0.0004)-0.137(0.421)-0.081(0.065)
Yes
Yes
101
0.07

Panel A and Panel B of Table 2 measure different types of rice productivity. The results in Table 2 indicate that in agricultural labor-intensive
districts with a higher share of land security measured by land registration,
land productivity is statistically significantly higher but labor productivity
is not. It might seem baffling that land registration affects land yield but has
no effect on labor productivity in agricultural labor-intensive districts. However, this difference indicates that in agricultural labor-intensive districts,
land registration changes land allocation to more productive farmers, promotes land reallocation to different activities, and increases investment thatmakes land more productive.

4.2

Robustness check: Land privatization on land yield

While the share of privately owned land has a statistically significant positive relationship with rice yield in agricultural labor-intensive districts, therelationship is not statistically significant in non-agricultural labor-intensive
districts. To provide a robustness check of these results, I explore the effectof the share of privately owned land per corregimiento on rice yield. Corregimiento is a less disaggregated level of data than district, allowing us tocontrol for within-district heterogeneity. As this is a cross-sectional analysis, the results of these estimations have to be taken with caution becauseof endogeneity concerns related to time-variant and -invariant unobservablecharacteristics. I mitigate this issue by including variables that control for
meteorologic, demographic, and income characteristics in each corregimiento.
I also include variables that indirectly control for soil characteristics.
The results in Column 1 of Table 3 show that the coefficient of the shareof privately owned land is positive and significant at the 1% level, highlighting that an increase in land privatization is associated with a rise in rice
yield. Column 1 also shows that an increase in rainfall by corregimiento
decreases rice yield. Furthermore, the coefficient of the share of irrigatedland is negative and statistically significant, indicating that an increase in
the share of irrigated land by corregimiento decreases rice productivity. The
most common type of rice cultivation in Panama is upland rice. Upland rice
is highly dependent on the weather conditions and type of soil. In Panama,corregimientos with higher shares of irrigated rice might have less suitablecharacteristics for rice production. Another statistically significant control
variable in Column 1 is the share of mechanized land: an increase in theshare of mechanized land is associated with a rise in rice yield.
Column 2 restricts the sample to agricultural labor-intensive corregimientos. The median share of workers in the agriculture sector corregimiento is42%. Hence, as before, I define as agricultural labor-intensive areas those13

Table 3: Effects of land privatization on log rice yield, OLS 2010
A: Log rice yield
Whole dataset Farmers>40% Farmers<40%
(1)
(2)
(3)
Share of privately owned land
0.26***
0.249**
0.199(0.101)(0.108)(0.195)Pop. Density-0.0001-0.003*-0.002*(0.0001)(0.002)(0.0001)
Illiteracy %0.003-0.0001-0.009(0.006)(0.006)(0.014)
Sex ratio-0.001
0.002-0.011**(0.004)(0.006)(0.005)Rainfall-0.00002*-0.00003*-0.00001(0.00001)(0.00001)(0.00003)
Share of irrigated land
-1.095***0.172-1.102***(0.241)(0.174)(0.198)
Share of mechanized land
1.761***
1.781***
1.686***(0.068)(0.108)(0.095)
Log median family income
0.121*
0.055
0.107(0.074)(0.089)(0.185)Observations 498
498
266
232
R-squared 0.038
0.496
0.52
0.525
Note: Robust standard errors are in parentheses.
***, **, and * stand for significance at 1%, 5%, and 10%.

14

areas where farmers represent 40% or more of the total working population.
The coefficient of the share of privately owned land in Column 2 is positive and statistically significant after controlling for a number of variables.
This finding shows that an increase in land privatization is associated with arise in rice yield in agricultural labor-intensive corregimientos. I repeat thesame analysis in Column 3, restricting the sample to non-agricultural laborintensive corregimientos. This time, the coefficient of the share of privately
owned land is positive but has no statistical significance, indicating that anincrease in land privatization in non-agricultural labor-intensive corregimientos is not statistically related to a rise in land productivity.
The results in the previous tables suggest that improving property rights
in agricultural labor-intensive areas in Panama affects allocative efficiency,
limits expropriation, and improves market transactions as in Besley andGhatak (2010). In both the corregimiento and the district cases, in agricultural labor-intensive areas, a higher share of complete land rights is associated with an increase in rice yield, while the relationship is not statistically significant in non-agricultural labor-intensive areas. In agriculturallabor-intensive areas, land privatization incentivizes farmers to be more productive. There might be better access to credit, higher incentives to invest inland improvements, land reallocation to more efficient activities and farmers,
and the reallocation of labor to other markets.

4.3

Mechanisms

I have shown that an increase in the share of privately owned land is associated with a rise in land productivity in agricultural labor-intensive districts.
The purpose of this section is to explore the possible mechanisms throughwhich complete land rights affect productivity. I use household-level data
to analyze the effect of land regularization on access to credit as well as on
land-attached, land mobile, and low cost investment.
I use pooled cross-sectional data from 1997 and 2008 at the household
level. This dataset comes from the LSMS, which surveyed 4,945 and 7,045
households in 1997 and 2008, respectively. I only use households working
either full- or part-time in the agriculture sector that considered themselves
to be landowners. The LSMS asks the following question: “Have you used
your own land for agricultural purposes in the last 12 months?” I restricted
the sample to households that answered yes to this question.
To measure the impact of land property rights on access to credit andinvestment, the specification isyit = βxit + δzit + γt + εit
15(3)

where yit is a dummy variable that takes the value of one if the household
received an agricultural loan or undertook an investment, and zero otherwise.
xit is a dummy variable that takes the value of one if the household possesses
a land title deed that is properly registered, and zero otherwise. zit controls
for household and plot characteristics and γt represents the year fixed effect.
I use probit and linear probability models. The previous specification has
two typical endogeneity concerns found in the literature on land rights andinvestment. The most common concern in this literature is reverse causality.
Sjaastad and Bromley (1997) argue that land insecurity may incentivize landattached investment to strengthen security. This would lead to overestimatedand biased coefficients. The best way in which to solve this issue is by using
instrumental variables. Common instruments include previous investment(Besley, 1995; Brasselle et al., 2002), historic characteristics (Banerjee andIyer, 2005), and modes of plot acquisition (Fenske, 2010). I deal with thisissue by restricting the sample to households that considered themselves to belandowners. This means that all households have high levels of land security
and that investment in land is not related to the certification and registrationof the land title. Another endogeneity concern is unobserved householdspecific characteristics. Deininger and Chamorro (2004) argue that farmers
with better access to credit or in a better investment environment are more
likely to register their land. I mitigate this concern by adding district andyear dummy variables into the specifications, under the assumption thathouseholds in the same district have similar access to credit and investment.
Although all the households used in the analyses considered themselves tobe landowners, only around 35% had a registered title and 10% had a possession rights certificate (derecho posesorio). Possession rights certificates areland use rights in Panama. These certificates can be exploited for agricultural purposes and transferred, but they cannot be used as collateral in theformal credit market. To obtain a possession rights certificate, the occupier
has to meet a number of requirements such as proving that the person has
settled the land for a long period of time and has low income.
4.3.1

Credit accessLand titling and privatization might increase access to credit, as farmers canuse their land as collateral for loans. This subsection explores the effect ofholding a registered title on applying for and obtaining loans. In Column 1 ofTable 4, I use a dummy variable that takes the value of one if the household
applied for an agricultural loan in the past 12 months as the dependent
variable. The variable of interest in Table 4 is registered title. In Column
1, the coefficient of registered title is positive and statistically significant,16

Table 4: Probability of applying for and obtaining agricultural loans

Registered title deed
Possession rights
Area
Area squaredYears of possession
Log household consumption
Year fixed effectDistrict fixed effectObservations(Pseudo) R-squared

Applying for loans(1): Probit(2): OLS
0.037***
0.037***(3.60)(0.012)0.026
0.022(1.63)(0.17)
0.001***
0.001***(6.04)(0.0002)-2.22E-06*** -3.39E-07***(-3.79)(1.06E-07)-0.0005-0.0002(-1.59)(0.0003)
0.021***
0.026***(4.25)(0.007)
No
Yes
No
Yes
2129
2129
0.128
0.085

Obtaining loans(3): Probit(4): OLS
0.038***
0.041***(3.83)(0.012)0.021
0.017(1.31)(0.015)0.001***
0.001***(5.85)(0.0002)-1.98E-06*** -3.03E-07***(-3.70)(1.02E-07)-0.0005-0.0002(-1.62)(0.0003)0.019***
0.024***(4.01)(0.007)
No
Yes
No
Yes
2129
2129
0.132
0.083

Notes: Probit regression coefficients are average marginal probabilities and robust
z-statistics are in parentheses. For the linear probability model, robust standard errors are in parentheses. ***, **, and * stand for significance at 1%, 5%, and 10%,respectively.

17

Table 5: Probability of having made land-attached and machinery investment

Registered title deed
Possession rights
Area
Area squaredYears of possession
Log household consumption
Year fixed effectDistrict fixed effectObservations(Pseudo) R-squared

Sheds
1: Probit
2: OLS
0.027**
0.036**(2.29)(0.015)-0.029-0.019(-1.25)(0.016)0.0003***0.001***(3.43)(0.0002)-2.27E-07*** -3.51E-07***(-2.81)(-1.13E-07)
0.001*
0.0001(1.69)(0.0004)
0.066***
0.058***(10.24)(0.008)
No
Yes
No
Yes
2129
2129
0.151
0.172

TrucksAgricultural sprayers
Tractors
3: Probit
4: OLS
5: Probit
6: OLS
7: Probit
8: OLS
0.017**
0.022*
0.076***
0.085***
0.002
0.006
(2.01)(0.11)(3.62)(0.024)(0.43)(0.006)0.002-0.006-0.021-0.0150.003
0.003(0.14)(0.010)(-0.63)(0.033)(0.43)(0.007)0.0004***0.0005**0.004***
0.001***
0.0002***0.0003*(3.24)(0.0002)(7.87)(0.0003)(2.67)(0.0002)-5.05E-07** -2.44E-07*** -7.39E-06 -5.63E-07*** 2.20E-07 -1.44E-07*(-1.96)(1.02E-07)(-5.85)(1.76E-07)(-1.74)(7.52E-08)-0.0005*-0.0005**-0.00020.0005-0.0002-0.0002(-1.78)(0.0003)(-0.37)(0.001)(-1.15)(0.0002)
0.046***
0.049***
0.114***
0.102***
0.016***
0.019***(8.19)(0.007)(11.20)(0.013)(4.47)(0.006)
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
2129
2129
2129
2129
2129
2129
0.288
0.183
0.105
0.189
0.269
0.097

Notes: Probit regression coefficients are average marginal probabilities and robust
z-statistics are in parentheses. For the linear probability model, robust standard errors are in parentheses. ***, **, and * stand for significance at 1%, 5%, and 10%,respectively.

using the probit model. Column 1 controls for plot-specific and household
characteristics, and the statistically significant control variables are area,
area squared, and consumption. I repeat the analysis by using the linear
probability model and fixed effects in Column 2. The coefficient of registeredtitle is statistically significant, indicating that farmers with registered titlesare 3.7% more likely to apply for an agricultural loan than their counterparts
without titles.
I use a dummy variable that takes the value of one if the household obtained an agricultural loan in the past 12 months as the dependent variablein Columns 3 and 4 of Table 4. In Column 3, the coefficient of registeredtitle is significant, using the probit model. Using the linear probabilities inColumn 4 confirms that the propensity to obtain agricultural credit is higher
for households with registered titles. Deininger and Chamorro (2004) pointout that farmers with better access to credit are more likely to formally register their land; hence, household-specific unobservable characteristics might
affect the results in Table 4. I mitigate this issue by including the district
and year fixed effects in the linear probability model and adding plot-specificand household-specific control variables.
These results contrast with the evidence from other Latin American countries in which land tenure security improvements do not translate into better
access to credit. For example, Carter and Olinto’s (2003) findings suggestthat there are no credit supply effects of tenure security on small farmers
in Paraguay. Similarly, Jansen and Roquas (1998) find that land titles are
not used as collateral; instead, producers mortgage their houses or their pro18

Table 6: Probability of having made investment

Registered title deed
Possession rights
Area
Area squaredYears of possession
Log household consumption
Observations
Pseudo R-squared

Seeds
Natural fertilizers(1)(2)-0.013-0.008(-0.83)(-0.55)0.019
0.010(0.81)(-0.45)0.0002-0.0001(0.83)(-0.50)-2.49E-07
4.57E-08(-1.63)(0.62)-0.001*0.0001(-1.92)(0.22)
0.057***
0.055***(7.19)(8.06)
2129
2129
0.042
0.048

Other fertilizers
(3)
0.014
(0.70)-0.013(-0.42)-0.0005**(-2.14)
2.53E-07**(2.10)-0.0006(-1.08)0.121***(13.53)2129
0.077

Pesticides(4)0.044**(2.02)-0.027(-0.80)0.0004(1.35)-5.41E-07**(-2.00)-0.0005(-0.81)0.079***(7.71)
2129
0.033

Notes: Probit regressions. Average marginal probabilities. Robust z-statistics are inparentheses. ***, **, and * stand for significance at 1%, 5%, and 10%, respectively.

duction in Honduras. In fact, the authors argue that few farmers actually
receive credit from formal institutions. In my specification, the data have no
information on whether households use their land as collateral or use theirhouses or production. Nonetheless, according to Columns 3 and 4 in Table
4, households with registered titles are around 3.8% more likely to obtainan agricultural loan. This might be one of the mechanisms through which
complete land rights improve rice yield.
4.3.2

Investment

Complete land rights might incentivize farmers to invest in machinery, land
improvements, and land conservation. Deininger and Chamorro (2004) findthat the registration of land titles increases the propensity to undertake investment in Nicaragua. I explore different types of investment that might
be associated with land privatization, using the probit and linear probability
models. First, I explore the probability of investing in different types of landattached and land mobile investment when households have complete land
rights. The dependent variables are dummy variables that take the valueof one if the household undertakes an investment, and zero otherwise. The
result in Column 1 of Table 5 shows that the coefficient of registered title
deed is positive and statistically significant. In Column 1, the dependent
variable is investment in sheds (a type of land-attached investment). The
propensity to undertake this investment increases by 2.7% when the title is19

properly registered. Column 2 confirms the investment-enhancing impact ofland registration on sheds, including the fixed effects. The possession rights
certificate coefficients are negative in Columns 1 and 2. Although farmers
with possession rights certificates have a higher degree of security than farmers lacking any certification, possession rights holders are no more likely toinvest in land-attached investment than title-less farmers.
Then, I focus on other types of investment, namely trucks, agricultural
sprayers, and tractors, which are all types of land mobile investment. The
coefficient of registered title is positive and statistically significant in Column
3, indicating that households with a registered title are around 1.7% more
likely to invest in trucks. Using the linear probability model in Column 4
confirms the results, but the effect is stronger. In Column 5, the coefficient ofregistered title indicates that households with complete land rights are 7.6%
more likely to invest in sprayers. The coefficient of registered title in Column
6 corroborates this result. Interestingly, the propensity to invest in tractors
does not increase for households with complete land rights, according to thecoefficients of registered title in Columns 7 and 8, which are not statisticallysignificant.
In Table 5, the coefficients of area and household consumption are statistically significantly in all estimations. This finding indicates that farmers
with larger plots and with a higher wellbeing status have a higher propensity
to undertake land-related investment. These results indicate that farmers
have high incentives to invest when they own the land title, knowing thatthe returns on investment will be earned by them.
Higher levels of land security may motivate farmers to invest in low costinvestment that improves land yield. Then, I focus on the following low costinvestment: seeds, natural fertilizers, other fertilizers, and pesticides. Column 1 of Table 6 suggests that having a registered title does not have any
statistical significance on the probability of investing in high yield seeds andplants. The coefficient of registered title in Column 2 is negative, but notstatistically significant. This finding indicates that registering the title does
not increase the propensity to invest in natural fertilizers. The dependent
variable of Column 3 is other fertilizers and, again, the coefficient of registered title is not statistically significant. This result confirms that householdswith a registered title are not necessarily more likely to invest in fertilizers
than their counterparts without such a title. The coefficient of registered title
is positive and statistically significant in Column 4, indicating that households with complete land rights are 4.4% more likely to use pesticides. The
coefficients of registered title in Columns 1, 2, and 3 are not statisticallysignificant. Household consumption is always significant, which shows thata household’s wellbeing increases the probability of buying low cost inputs.
20

The results of this section suggest that the main channels through which
land registration affects agricultural productivity are loans, investment inmachinery, land-attached investment, and the higher use of pesticides.

5

Concluding remarks

This study empirically investigated the impact of land property rights on
agricultural productivity in Panama. The findings show that an increase inland rights in agricultural labor-intensive districts is associated with a rise inland productivity. Then, it used LSMS household-level data to explore potential mechanisms through which property rights affect productivity, findingthat households with registered land titles are more likely to obtain agricultural credit, undertake land-attached and land mobile investment, and use
pesticides. Owing to data constraints, this study could not investigate theimpact of more secure property rights on land transfers, which could be asignificant mechanism.
The impact of property rights or land titling programs on the agriculture
sector in Latin America has been repeatedly studied; however, these studies
have found different results. In line with this research, other studies also finda positive impact of land titling on agricultural outcomes in Latin America.
For example, in Nicaragua, a land titling and registration program resulted
in an increase in land-attached investment (Deininger and Chamorro, 2004).
Further, the lack of a land title is a strong disincentive to participate in therental market in Nicaragua (Deininger et al., 2003). In Mexico, De Janvry etal.’s (2015) findings suggest that land certification allows for more efficientland and labor allocation, while in Paraguay, tenure security has a strong
effect on demand for land-attached capital. However, these effects might
only benefit large-scale producers (Carter and Olinto, 2003).
Some studies have not found a positive effect of titling in the agriculture
sector. For example, Jansen and Roquas (1998) argue that a land titling program in Honduras has not led to an increase in investment and productivity,
instead exacerbating land-related conflicts. Further, the major expansion ofland titles in Nicaragua and Honduras did not increase formal credit market
participation (Boucher et al., 2004). There might be two reasons why the
results of these two previous studies are different from the findings of thepresent study. First, in Panama, land registration increases tenure security,
while in Honduras, according to Jansen and Roquas (1998), the title program was poorly implemented and did not increase land security. Second,in the majority of Central American countries, rural credit markets are insufficiently evolved and thus property rights do not improve credit access.
21However, Panama has a well-functioning financial system, and there might
be access to formal credit in rural areas. Indeed, in Panama, agricultural
credit has increased in recent years. This fact, along with an investmentfriendly environment, might be why an increase in land rights in agriculturallabor-intensive districts is associated with a rise in land productivity.

Appendix
This section explains the variables used in the analyses and summary statistics at the household level. Table A1 defines all the variables used in thisstudy. Table A2 presents the summary statistics of the household-level data.
Table A1: Variable definitions
A: District level
Rice harvested in hundredweight divided by the area of net rice sown (in hectares)
per district.
Labor productivity
Rice harvested (in hundredweight) divided by the number of farmers per district.
Share of privately owned landThis is the variable of interest in the baseline, which captures the effect of land rights on
Productivity
This is the share of agricultural land properly registered and certified to total agriculturalland per district.
Pop. density
The number of inhabitants per square kilometer in a district.
Illiteracy
Percentage of illiterate inhabitants per district.
B: Corregimiento level
Rice yield
Rice harvested in hundredweight divided by the area of net rice sown (in hectares)
per corregimiento.
Labor productivity
Rice harvested (in hundredweight) divided by the number of farmers per corregimiento.
Share of privately owned landThis captures the effect of land rights on productivity. It is the share of agriculturalland properly registered and certified to total agricultural land per district.
Pop. density
The number of inhabitants per square kilometer in a corregimiento.
Illiteracy
Percentage of illiterate inhabitants per corregimiento.
Sex ratio
The number of men per 100 women by corregimiento.
Rainfall
Mm of rain by corregimiento. Panama has 109 meteorological stations that measure theamount of rain. I used the data from the closest station if the corregimiento did not have one.
In the case the corregimiento had more than one meteorological station, the average was used.
Share of irrigated land
The share of rice land irrigated per corregimiento.
Share of fertilized land
The share of land used for rice plantations that has used fertilizers per corregimiento.
Share of mechanized land
The share of rice land that has been mechanized per corregimiento.
Log median family income
The natural logarithm of the median income of families per corregimiento
Share of workers in agriculture
Share of farmers to total workers per corregimiento.
Share of farms with loans
The share of farms that received a loan per corregimiento.
Share of farms that used machines The share of farms that invested in machinery per corregimiento.
C: Household level
Applying for loans
Dummy variable. Whether the head of the household has applied for an agriculturalloan in the past 12 months.
Obtaining loans
Dummy variable. Whether the head of the household has obtained an agriculturalloan in the past 12 months.
Registered title deed
Dummy variable that takes the value of one if the household has a title deed completely
registered and certified, and zero otherwise.
Possession rights
This takes the value of one if the owner has a title but it is not registered, and zero otherwise
Area
Area of land used for agricultural purposes in hectares.
Years of possession
The number of years that the household has used the land.
Log household consumption
Log of household consumption using 1997 US dollars.
Tractors
Dummy variable that takes the value of one if the household has a tractor to use on the land, and zero otherwise.
TrucksDummy variable that takes the value of one if the household has a truck used for agricultural purposes, and zero otherwise.
Agricultural sprayers
Dummy variable that takes the value of one if the household has used a sprayer on the land, and zero otherwise.
Sheds
Dummy variable that takes the value of one if the household has built a shed on its land, and zero otherwise.
Seeds
Dummy variable that takes the value of one if the household has bought high yield seeds or plants in the past year, and zero otherwise.
Natural fertilizers
Dummy variable that takes the value of one if the household has used natural fertilizers in the past year, and zero otherwise.
Other fertilizers
Dummy variable that takes the value of one if the household has used other fertilizers in the past year, and zero otherwise.
Pesticides
Dummy variable that takes the value of one if the household has used natural pesticides in the past year, and zero otherwise.

Rice yield

22

Table A2: Summary statistics at the household level

Applying for loans
Obtaining loans
Registered title deed
Possession rights
Area
Years of possession
Log household consumption
Tractors
TrucksAgricultural sprayers
Sheds
Seeds
Natural fertilizers
Other fertilizers
Pesticides

Mean Std. Dev. Min.
0.053
0.225
0
0.049
0.216
0
0.352
0.478
0
0.103
0.303
0
20.3351 95.2341
.01
21.75
15.40
0
6.714
.961
3.669
0.0131
0.114
0
0.039
0.193
0
0.35
0.477
0
0.085
0.279
0
0.122
0.327
0
0.096
0.295
0
0.226
0.418
0
0.295
0.456
0

Max
1
1
1
1
2500
92
10.455
1
1
1
1
1
1
1
1

References
Atwood, D. A. (1990), ‘Land registration in africa: The impact on agricultural production’, World Development 18(5), 659–671.
Banerjee, A. & Iyer, L. (2005), ‘History, institutions, and economic performance: the legacy of colonial land tenure systems in India’, American
Economic Review 95(4), 1190–1213.
Barraclough, S. L. (1970), ‘Agricultural policy and land reform’, Journal of
Political Economy 78(4, Part 2), 906–947.
Besley, T. (1995), ‘Property rights and investment incentives: Theory andevidence from Ghana’, Journal of Political Economy 103(5), 903–937.
Besley, T. & Ghatak, M. (2010), Chapter 68 - Property rights and economic
development, in D. Rodrik & M. Rosenzweig, eds, ‘Handbooks in Economics’, Vol. 5 of Handbook of Development Economics, Elsevier, pp. 4525
– 4595.
Boucher, S. R., Barham, B. L. & Carter, M. R. (2005), ‘The impact of23

market-friendly reforms on credit and land markets in Honduras andNicaragua’, World Development 33(1), 107–128.
Brasselle, A.-S., Gaspart, F. & Platteau, J.-P. (2002), ‘Land tenure security
and investment incentives: puzzling evidence from Burkina Faso’, Journalof Development Economics 67(2), 373–418.
Carter, M. R. & Olinto, P. (2003), ‘Getting institutions right for whom?
credit constraints and the impact of property rights on the quantity andcomposition of investment’, American Journal of Agricultural Economics
85(1), 173–186.
Chankrajang, T. (2015), ‘Partial land rights and agricultural outcomes: evidence from Thailand’, Land Economics 91(1), 126–148.
De Janvry, A., Emerick, K., Gonzalez-Navarro, M. & Sadoulet, E. (2015),‘Delinking land rights from land use: Certification and migration in Mexico’, American Economic Review 105(10), 3125–3149.
De Janvry, A. & Sadoulet, E. (1989), ‘A study in resistance to institutionalchange: the lost game of Latin American land reform’, World Development
17(9), 1397–1407.
Deininger, K. & Chamorro, J. S. (2004), ‘Investment and equity effects of land
regularisation: the case of Nicaragua’, Agricultural Economics 30(2), 101–
116.
Deininger, K. & Feder, G. (2009), ‘Land registration, governance, and development: Evidence and implications for policy’, The World Bank Research
Observer 24(2), 233–266.
Deininger, K., Zegarra, E. & Lavadenz, I. (2003), ‘Determinants and impactsof rural land market activity: Evidence from Nicaragua’, World Development 31(8), 1385–1404.
Feder, G. & Feeny, D. (1991), ‘Land tenure and property rights: Theory andimplications for development policy’, The World Bank Economic Review
pp. 135–153.
Fenske, J. (2010), ‘L’etranger: status, property rights, and investment incentives in Cote D’ivoire’, Land Economics 86(4), 621–644.
Fenske, J. (2011), ‘Land tenure and investment incentives: Evidence from
West Africa’, Journal of Development Economics 95(2), 137–156.
24

Fort, R. (2008), ‘The homogenization effect of land titling on investment incentives: evidence from Peru’, NJAS-Wageningen Journal of Life Sciences
55(4), 325–343.
Garcı́a, J. M. F. (1970), ‘Contrarreforma agraria y catastro rural en Panamá’,
Revista de Economı́a Polı́tica (54), 134.
Goldstein, M. & Udry, C. (2008), ‘The profits of power: Land rights and agricultural investment in Ghana’, Journal of Political Economy 116(6), 981–
1022.
Hausmann, R., Espinoza, L. & Santos, M. A. (2016), ‘Shifting gears: A
growth diagnostic of Panama’.
Jansen, K. & Roquas, E. (1998), ‘Modernizing insecurity: The land titling
project in Honduras’, Development and Change 29(1), 81–106.
Lipton, M. (2009), Land reform in developing countries: Property rights andproperty wrongs, Routledge.
Migot-Adholla, S., Hazell, P., Blarel, B. & Place, F. (1991), ‘Indigenous land
rights systems in sub-Saharan Africa: a constraint on productivity?’, The
World Bank Economic Review 5(1), 155–175.
Sepúlveda, C. (1967), ‘Reflexiones sobre la alianza para el progreso’, Foro
Internacional 8(1 (29), 68–79.
Shearer, E. B., Lastarria-Cornhiel, S., Mesbah, D. et al. (1991), The reform of
rural land markets in Latin America and the Caribbean: research, theory,and policy implications, number 141, Land Tenure Center, University of
Wisconsin-Madison.
Sjaastad, E. & Bromley, D. W. (1997), ‘Indigenous land rights in SubSaharan Africa: Appropriation, security and investment demand’, World
Development 25(4), 549–562.

25