Journal of Business and entrepreneurial
April - June Vol. 7 - 2 - 2023
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e-ISSN: 2576-0971
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A comparative study of rice export levels before
and during dollarization 1980-2020
Estudio comparativo de los niveles de exportación de arroz
antes y durante la dolarización 1980-2020
Jorge Wilson Flores Rodríguez
*
Jorge Washington Encalada Noboa
*
Angie Yohan Larrosa Larrosa
*
Daisy Priscila Criollo Rocohano
*
ABSTRACT
This research focuses on a comparative study
between the levels of rice exports before and during
dollarization, corresponding to the years 1980-2020;
thus, the information from the World Bank, Central
Bank of Ecuador and SENAE is used as a data base,
which eventually leads us to a non-experimental
research with a quantitative approach and through
the t-test was able to determine the existence of a
significant change in rice exports before and during
dollarization. On the other hand, the coefficient of
determination allows us to appreciate that with
dollarization there is a 10% incidence between price
and export levels, as well as establishing that during
this stage, the dependence on international relations
and the high production costs influence the degree
of competitiveness of the price of rice.
Keywords: Dollarization, Price, Rice Exports
ABSTRACT
* Specialist in Development Projects, University of Guayaquil
wilson.floresr@ug.edu.ec, https://orcid.org/0000-0002-7436-
7441
* Specialist in Development Projects, University of Guayaquil,
jorge.encaladan@ug.edu.ec
https://orcid.org/0000-0002-2884-5596
* Agricultural Economist, Unidad Agraria del Ecuador,
alarrosal@uae.edu.ec
https://orcid.org/0000-0003-1381-5762
* Degree in Mathematics and Physics Pedagogy, María Luisa
Luque de Sotomayor Educational Unit.
daisy.criollor@ug.edu.ec, https://orcid.org/0000-0002-4111-
5635
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La presente investigación se enfoca en realizar un
estudio comparativo entre los niveles de exportación
de arroz antes y durante la dolarización,
correspondientes a los años 1980-2020; es así que se
recurre a tomar como base de datos la información
del Banco Mundial, Banco Central de Ecuador y
SENAE, misma que eventualmente nos lleva a una
investigación no experimental con enfoque
cuantitativo y que mediante la prueba t pudo
determinar la existencia de un cambio significativo en
las exportaciones de arroz antes y durante la
dolarización. Por otro lado, el coeficiente de
determinación permite apreciar que con la
dolarización hay un 10% de incidencia entre el precio
y los niveles de exportación, así como también se
establece que durante esta etapa, la dependencia de
las relaciones internacionales y los elevados costos
de producción influyen en el grado de competitividad
del precio del arroz.
Keywords: Dolarización, Precio, Exportación de
Arroz
INTRODUCTION
Globalization has allowed the exchange of products, knowledge and even culture
worldwide, being the export the activity that promotes and reactivates it constantly, it
is the decision of each country to verify its resources and exploit them to the maximum,
to obtain higher levels of income. If we analyze it geographically, Ecuador has enough
resources to obtain a star export product such as rice, for example; which being a basic
necessity within the country, has a high level of occupation for its production in the
different provinces, mainly in Los Rios and Guayas. In addition, According to De
Bernardi, 2017, rice is a cereal that is occupying the second place in production
worldwide after corn.
However, rice production cannot be fully exploited, since it is not feasible to produce
in large quantities if there are not enough buyers, since exports are not only Ecuador's
source of income, it can be considered that all of Latin America puts it into practice and
that is why the producers of different foods (oil, cocoa, rice, bananas, shrimp, etc.) are
in constant competition to receive buyers.
This is where the differences between the levels (higher or lower) of export that each
country has, because the importer is mainly fixed in the prices offered and based on this
establishes the pact to form a new partnership. It is the seller's responsibility to find a
way to ensure that his product is in great demand, which is why other countries devalue
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32
their currency to attract consumers and automatically convert the price they propose
into the most promising price in the international market.
Although the strategy imposed by other countries is neither wrong nor limited to be
used, Ecuador cannot make use of it, since the currency it has is not its own and this
prevents it from optimizing sales prices. This is also stated by Dávalos, 2004, who
mentions that "since Ecuador implemented dollarization, it is tied in a certain way to
monetary policies, since it is not independent". So, is there a variation in rice export
levels before and during dollarization?
International trade and price competitiveness are some of the factors that place Ecuador
at a disadvantage when exporting. Exports from the agricultural area have become a
dynamism for Ecuador's economy because they allow maintaining a trade balance in the
face of dollarization, just the shock of solvency due to the acquisition of foreign
currency. Therefore, we intend to measure whether the fluctuation in rice prices has
significantly affected rice export levels. Considering the implementation of dollarization
as a turning point, and thus determine if this is an element that directly influences rice
exports.
MATERIALS AND METHODS
In this research work, the hypothetical deductive method is used, since the general
information found in secondary sources such as the Central Bank of Ecuador (BCE) and
the National Customs Service of Ecuador (SENAE), was registered and organized
through statistical tables and graphs, which allowed obtaining specific conclusions
regarding each variable. In addition, correlational research was used to verify the
influence between the independent variable "factors affecting rice exports" and the
dependent variable "export levels".
Since there was no manipulation of the variables, the study is considered non-
experimental, while descriptive research is used to gather information related to the
phenomenon, essentially to determine the factors that affect export levels.
RESULTS
Interpret the historical behavior of export levels before and during dollarization.
Tons of rice exported by year.
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Figure 1. Rice export levels in tons 1980-2020.
Source: Prepared by the author, 2021.
During the years 1993 and 1997, rice exports showed an unprecedented increase, and
then there was a drop until 2000, when dollarization was applied, and then between
2004 and 2007 exports reached their highest peak, followed by a drop in 2008, and
continued with irregular fluctuations in the following years.
Real exchange rate
Figure 2. Real Exchange Rate Sucres per Dollar 1980-2020
Source: Central Bank of Ecuador: 90 years of statistical information.
Before dollarization, the Central Bank of Ecuador (BCE) was the government entity in
charge of issuing the currency. In 1980 a dollar in Ecuador cost 25 sucres, since then its
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price has been rising slowly but steadily. By the year 2000, 25 thousand sucres were
needed to obtain one dollar, at which time the country was registering the highest
inflation in its history. According to Villalba (2019), the excessive printing of sucres
resulted in an oversupply of the currency, which in turn caused monumental inflation.
The lack of control was caused by several factors, among them the "sucretization",
which consisted of an uncontrolled issuance of currency to save businessmen who had
high debts in dollars with foreign suppliers. The measure was taken by the State in
collusion with private banks to try to rescue a deficient economic and productive
structure marked by the already unstable public policies. To identify the factors affecting
rice export levels between 1980-2020. The following are the elements that determined
the cost of rice after dollarization.
Production costs.
Table 1. Rice Production Costs (LCU and Dollars per Tons)
1980
1981
1982
1983
1984
1985
1986
1987
5630
6790
7620
1381
0
1926
0
2722
0
34020
23740
225,20
271,6
0
254,0
0
313,8
6
305,7
1
388,8
6
276,59
139,6
5
1990
1991
1992
1993
1994
1995
1996
1997
127060
1587
70
2622
40
2844
40
3681
70
4950
00
60800
0
90200
0
165,44
151,7
9
170,9
5
148,2
2
167,5
8
193,0
6
190,66
25,61
2000
2001
2002
2003
2004
2005
2006
2007
160
136
130
149
226
191
165,4
238,2
160,00
136,0
0
130,0
0
149,0
0
226,0
0
191,0
0
165,40
238,2
0
2010
2011
2012
2013
2014
2015
2016
2017
260
340
347,7
355,3
363
385,9
362,7
344,9
260,00
340,0
0
347,7
0
355,3
0
363,0
0
385,9
0
362,70
344,9
0
Note: Source: FAO.org Prepared by the Author
According to MAGAP (2013), high costs affect rice production and exports. While in
an average country the cost of operations is 20.2 % and raw material is 21.9 %, in
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Ecuador it is around 24.9 %, 3 percentage points above the average. In addition, labor
costs have increased 3.2% since 2000 as a direct effect of dollarization. The price of
transportation must also be taken into account, since once dollarized, Ecuador
experienced a significant increase in fuel costs. Currently, the local currency requires
higher levels of efficiency so that the price of rice is competitive in terms of international
prices with respect to neighboring countries.
The rice price history is also presented, which is an indicator directly associated with
costs and affects exports:
Rice price per sack 1980-2020
Source: Prepared by the author, 2021.
It can be seen in the graph that the behavior of the price of rice before dollarization has
fluctuated but with a downward trend, and as of the year 2000 there has been a gradual
growth with an upward trend. Clearly, by adopting the dollar as the currency, this effect
is produced since prices and costs do not depend on the value of the country's own
currency.
International rice price
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Graph 4. International rice price 1980-2020
Source: FAO.org Prepared by the Author
On the other hand, it can be observed that the cost of rice at the international level has
had its ups and downs, in the first part (before dollarization) the highest price was $79,
but after dollarization it had a higher increase, reaching $112 in 2005.
Graph 5. Inflation (1980-2019)
Source: FAO.org Prepared by the Author
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Social skepticism towards the new currency and the impossibility of issuing it within the
national territory caused speculation in the market, shortages of the currency, rounding
of prices above cost and, consequently, an inflation of 10% during the first quarter of
2000. Almost two years after the adoption of the U.S. currency, Ecuador managed to
stabilize its annual inflation levels to single digits. From 2004 onwards, some highs and
lows have been due to mismatches between supply and demand.
Farmers point out that at present, both domestic and imported inputs needed for
production are continually increasing in price. The amount of products that are available
on the market is often due to the timing of harvests, which could be altered by adverse
weather conditions, which in turn cause shortages, increased prices for foodstuffs and
transportation.
According to MAGAP (2015), foreign trade policies affect rice exports. Therefore, it is
vitally important to have excellent international relations because through them it is
possible to maintain an adequate flow of a currency whose origin is foreign and without
which the domestic economy could be in serious trouble. This aspect is related to the
acquisition of technologies, raw materials, machinery and equipment to optimize rice
production processes, as well as commercial alliances to facilitate the exchange of this
product.
To evaluate the relationship between rice export levels and the factors that affect it
during the years 1980-2020. Next, the relationship between the international price of
rice and production costs (independent variables) and rice export levels (dependent
variable) in Ecuador between 1980-2020 will be established, for which a multiple linear
regression was performed using the Grettel statistical program.
First, the stationarity of the variables is determined, to verify whether they are
stationary in levels or have some order of integration different from zero, given that
they are time series and there is a risk of finding a spurious regression if the variables
are not stationary or are not cointegrated.
To certify the stationarity of the dependent variable "Exports", the Augmented Dickey-
Fuller test was applied in levels, and its result showed a p-value of 0.0238, so it is not
stationary in levels, since it was detected that it has a unit root. Continuing with the
independent variable "International Price", the same test was applied, yielding a result
of 0.275; therefore, it was verified that it is not stationary in levels and has a unit root.
In the same way we proceeded with the independent variable "Production Cost"
resulting in a p-value of 0.788, also determining that it has a unit root and is not
stationary in levels.
Therefore, the Engle-Granger cointegration test is applied before running the regression
to find out if the variables cointegrate and, therefore, there is no risk of finding a
spurious regression.
Below we can observe different p-values of the individual significance t-test, to
determine the statistical significance of the coefficients of each variable. These data
indicate that the variable "INT PRICE" has a coefficient equal to 0.000, that is, it does
not have a statistically significant relationship, unlike the independent variable
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"COSTOPROD" whose coefficient is 0.0188, demonstrating the individual significance.
It can be observed then that there is a direct relationship between rice production costs
and export levels, rejecting the international price as a variable that directly affects
exports.
Similarly, it can be noted that the R2 of 0.7127 is the percentage of variability of the
dependent variable, i.e. 71.2% of rice exports is explained by the cost of production. In
order to demonstrate that the results obtained are statistically valid, we proceed to test
the regression assumptions. First we have the linearity contrast, it will be demonstrated
that the linear equation obtained is adequate to explain the dependent variable, we
proceeded to run the RAMSEY RESET test, this test demonstrates that the linear
specification is sufficient to explain the variability of the dependent variable, given that
the p value of the test is above the significance level, that is to say that a linear
specification is well used.
The Contrast of normality of the errors was also applied, applying the JARQUE-BERA
test, in which we observed that the p-value is 0.000 and tells us that the sample has a
non-normal behavior. Applying the WHITE test, the homoscedasticity contrast of the
errors was determined, where the p-values were 0.00 and 0.00, therefore there is no
homoscedasticity. Contrast of no serial autocorrelation of the residuals, applying the
Serial Correlation LM Test with 2 lags, we have a p-value of 0.68 and 0.65 so we do not
reject H0, that is to say that the errors do not present serial correlation of order 1 or
2.
In the multicollinearity analysis of the model, applying the Inflated Variance Factor test,
the international price obtained 1.0105 and production costs 1.0105. Therefore, there
is no multicollinearity, i.e., there is no correlation between them.
Since the tests were not positive and the correlation assumptions such as
homoscedasticity and normality of the data were not met, we proceeded to perform
another analysis considering rice exports before and during dollarization using t-
Student.
The following table shows the annual rice export table by tons during the period 1980-
2020 with the percentage variation rate
Table 2. Annual rice exports by tons and percentage variation rate.
Expo/ton before
Expo/ton Des
Rate before
Rate of
0,00
11703,00
0
5,6816201
0,00
78195,00
0
-0,66559243
0,00
26149,00
0
0,44464415
0,00
37776,00
0
-0,95605676
11,00
1660,00
-1
17,9048193
0,00
31382,00
0
4,13144478
2189,00
161035,00
12,53677478
-0,37816624
29632,00
100137,00
-0,985826134
-0,94588414
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420,00
5419,00
58,52380952
-0,05683705
25000,00
5111,00
-0,3944
3,11113285
15140,00
21012,00
0,034280053
1,01418237
15659,00
42322,00
-0,209911233
-0,65930249
12372,00
14419,00
-0,74862593
1,99750329
3110,00
43221,00
8,729903537
-0,62830568
30260,00
16065,00
-0,135723728
-0,93071895
26153,00
1113,00
2,421481283
-0,81042228
89482,00
211,00
0,248452203
1,11848341
111714,00
447,00
-0,515226382
69,5950783
54156,00
31556,00
-0,444641406
-0,08014324
30076,00
29027,00
-1
-1
558%
510%
Note: Prepared by the Author
Using these data, a T-Student is performed to determine if there is a significant
difference in a single sample, i.e., before dollarization, in the period 1980-1999 and
during dollarization, the period 2000-2020, obtaining the following data:
Table 3. Descriptive statistics for a sample
N
Media
Deviation Deviation
Avg. error
Exports in
tons
40
27583,35
35592,847
5627,723
Note: Prepared by the Author
Table 4
Paired two-sample t-test for paired sample means
2189
12,53677
Media
33367,697
4,963641
4
Variance
1346679101
247,8758
1
Remarks
33
33
Pearson correlation coefficient
-0,2972279
Hypothetical difference of means
0
Degrees of freedom
32
Statistic t
5,22193089
P(T<=t) one tail
0,000
Critical value of t (one-tailed)
1,694
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P(T<=t) two-tailed
0,000
Critical value of t (two-tailed)
2,03693334
Note: Prepared by the Author
It can be noted that if there is a difference in the mean, because the significance level is
0.000, the researcher's hypothesis is accepted, in this case, rice exports show significant
changes.
Table 5. Correlation matrix of variables
PreIntA
rr
TipCamS
uc
CostProd
Arr
CostProdAr
US
InflPorcent
1,0000
0,1195
0,1887
-0,1022
-0,2506
PreIntArr
1,0000
0,9600
-0,3682
0,3184
TipCamSuc
1,0000
-0,3523
0,3140
CostProdArroz
LC
1,0000
-0,5092
CostProdArroz
US
1,0000
InflPorcent
PrecArrfincaS
ac
ExportTo
n
-0,1928
0,8160
PreIntArr
-0,4028
0,1995
TipCamSuc
-0,3912
0,2822
CostProdArr
0,8801
-0,2989
CostProdArUS
-0,4052
-0,1215
InflPorcent
1,0000
-0,3330
PrecArrfincaSac
1,0000
ExportTon
Note: Prepared by the Author
Therefore, it is evident that the independent variables, international price and real
exchange rate, are positive figures, this means that the correlation is positive with 81%
and 19% of exports, as an analysis we say that the international price should maintain a
positive balance so that exports do not decrease and thus consumers will always be
willing to purchase them. However, the cost of production, the price per bag and
inflation have a negative correlation, which means that when inflation increases, we have
an unfavorable impact on the costs and price of rice, which has a direct impact on rice
exports.
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Table 6. Model 1: OLS, using the observations 1980-2019 (T = 40)
Dependent variable: exports in tons
Coefficient
Standard
deviation
Statistic t
p-value
PrecIntArroz
1102,70
90,4374
12,19
<0,0001
**
*
Average of the vble.
dep.
27583,35
D.T. of the vble. dep.
35592,85
Sum of quad. residues
1,66e+10
T.D. of regression
20626,08
R-square not centered
0,792187
R-square centered
0,664179
F(1, 39)
148,6686
p-value (of F)
7,02e-15
Log-likelihood
-453,6237
Akaike Criteria
909,2473
Schwarz Criteria
910,9362
Hannan-Quinn Crit.
909,8579
rho
0,207710
Durbin-Watson
1,583015
Note: Prepared by the Author
The adjusted R-Squared shows a coefficient of 0.66 which indicates that there is a
dependence between these variables (international price and rice exports) and since the
statistical significance level is less than 0.0001, it indicates that it is significant.
Contrast of normality of residuals -
Null hypothesis: [The error has Normal distribution].
Contrast statistic: Chi-square (2) = 17.3676
With p-value = 0.00016931
The normality of the residuals in this model is shown to be normal because the p-value
is non-zero.
White's heterocedasticity contrast -
Null hypothesis: [No heteroscedasticity].
Contrast statistic: LM = 10.6475
With p-value = P (Chi-square (2) > 10.6475) = 0.00487448
The test indicates that there is no heteroscedasticity demonstrated with a p-value
different from zero, in this case 0.00487.
LM contrast of autocorrelation up to order 1 -
Null hypothesis: there is no autocorrelation.
Contrast statistic: LMF = 1.7451
With p-value = P (F (1, 38) > 1.7451) = 0.194394
This test shows that the variables analyzed are not autocorrelated, since the
contrast statistic indicates that the p value of this indicator is 0.19.
Normality of waste
Frequency distribution for residual, observations 1-40
Number of boxes = 7, Mean = -982.559, Avg.typ.dev.=20602.1
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Interval midpoint frequency rel accum.
< -74437, -84567, 1 2,50% 2,50%
-74437, - -54176, -64306, 0 0,00% 2,50%
-54176, - -33915, -44046, 3 7,50% 10,00% **
-33915, - -13655, -23785, 3 7,50% 17,50% **
-13655, - 6605,7 -3524,6 20 50,00% 67,50% ******************
6605,7 - 26866, 16736, 12 30,00% 97,50% **********
>= 26866, 36997, 1 2,50% 100,00%
Contrast of the null hypothesis of Normal distribution:
Chi-square (2) = 17.368 with p-value 0.00017
Regarding the distribution of the model's residuals, it can be said that they have a
normal distribution, as evidenced by the p coefficient, with 0.00017, which, being
different from zero, rejects the null hypothesis and accepts the alternative hypothesis.
Heterocedasticity
White's heteroscedasticity test
OLS, using the observations 1980-2019 (T = 40)
Dependent variable: uhat^2
Coefficient Standard Deviation t statistic p-value
-------------------------------------------------------------------------------------------------
const -1.59793e+08 2.96929e+08 -0.5382 0.5937
PriceInternational~ 2.04954e+07 1.76053e+07 1.164 0.2518
sq_Internal_Price~ 33566.7 183602 0.1828 0.8559
R-squared = 0.266187
Contrast statistic: TR^2 = 10.647483,
With p-value = P (Chi-square (2) > 10.647483) = 0.004874
In this case, the error heteroscedasticity test shows that there is homoscedasticity
because the r-squared coefficient is non-zero (0.004874), i.e., the error variance is
constant. If the error variance is not constant across observations, the regression is
heteroscedastic.
Autocorrelation
Breusch-Godfrey contrast of first-order autocorrelation
OLS, using observations 1980-2019 (T = 40)
Dependent variable: uhat
Coefficient Standard deviation t statistic p-value
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---------------------------------------------------------------------
PriceInternational~ -16.9177 90.4964 -0.1869 0.8527
uhat_1 0.211955 0.160448 1.321 0.1944
R-squared = 0.043907
Contrast statistic: LMF = 1.745101,
With p-value = P (F (1.38) > 1.7451) = 0.194
Alternative statistic: TR^2 = 1.756293,
With p-value = P (Chi-square (1) > 1.75629) = 0.185
Ljung-Box Q' = 1.80767,
With p-value = P (Chi-square (1) > 1.80767) = 0.179
This test shows that the variables analyzed are not autocorrelated, since the contrast
statistic indicates that the p-value of this indicator is 0.19.
Distributed lag model
The Ordinary Least Squares model is applied:
Table 7. Distributed Lag Models
MCO reg Exp L1. Exp L2. Exp Price
Source
SS
df
MS
Number of obs:
39
Model
1.4839e+10
3
4.9462e+09
F(3, 35)
5.22
Prob > F
0.0044
Residual
3.3141e+10
35
946881443
R-squared
0.3093
Adj R-squared
0.2501
Total
4.7980e+10
38
1.2626e+09
Root MSE
30771
Exp
Coef.
Std. Err.
t
P>||t|
[95% Conf. Interval] [95%
Conf.
L1.
.4884583
.1560378
3.13
0.004
.1716848 .8052318
L2.
-.2750368
.1564905
-1.76
0.088
-.5927293 .0426557
Price
-1445.296
689.9119
-2.09
0.043
-2845.891 -44.70002
_cons
56439.97
18380.13
3.07
0.004
19126.33 93753.6
Lag* reg Exp L1. Exp L2. Exp Price
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Figure 6. OLS- Distributed lags (1980-2019)
Source: Prepared by the Author
Here we can observe the least squares as the dependent variable, while rice exports
and price as the independent variable; since the "probability" value is less than 0.05, it
means that this independent variable (price) has a significant impact on the dependent
variable (rice export levels). It can also be seen that the R-squared value is 0.1044, which
means that the variables within the study are related by 10%.
Discussion
By interpreting the historical behavior of export levels before and during dollarization,
it could be revealed that the exchange rate or the value of currencies, within
international trade, is a vital point; because in every international commercial
transaction it is required to establish an exchange rate. These statements coincide with
Fiallo (2017), who pointed out the importance of the agricultural sector in a dollarized
economy where agriculture reaches 9% (Gross Domestic Product of the Country)
helping to comply with food sovereignty, it also reaches 26.8% in terms of
representation of the economically active population of Ecuador. On the other hand,
Lascano and Robinzon (2018), state that rice exports are fundamental, especially
towards the Chinese market for small producers of the Gloria de Dios Association.
Likewise, when identifying the factors that affect rice export levels between 1980-2019,
production costs can be highlighted, since they do not remain constant due to changes
in labor, raw materials, machinery and assemblies. In addition, with dollarization these
costs had an inflation, as it caused speculations in the market, currency shortages,
rounding of prices above cost and finally a breakdown in the international relationship,
the latter being one of the most important according to MAGAP (2015) who points out
that through international relations it is possible to maintain an adequate flow of a
currency whose origin is foreign and without which the domestic economy could be in
serious trouble.
It is also known that rice production is a process of change and potential, as producers
are currently in search of new markets, since current markets are unstable and
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dwindling, causing the maintenance and increase of rice production to be very
competitive.
Rice occupies 17% of the arable land (including both permanent and transitory crops)
and is the most important transitory crop, with at least 40% of the area planted. Rice
accounts for 12% of agricultural GDP and 0.8% of GDP at the national level, however,
rice production in Ecuador as a whole does not exceed the 4 MT/ha line, despite the
increasingly extensive sown soils which places the country slightly below the world
average according to the study conducted between 2002 and 2011. Zambrano et. al
(2019) do not agree with the above, for them, SME exports depend on factors such as
production levels, legislation, innovation, market purchasing power, unemployment,
financing options, competitiveness and international investment.
CONCLUSIONS
When interpreting the historical behavior of export levels before and during
dollarization, it was found that the value of currencies in international trade is a vital
point and if a greater number of units of other currencies are required to acquire a
single currency, it is said that it is overvalued and as a consequence the prices of the
goods and services that this country sells will be artificially increased, thus making it less
competitive in international prices. In addition, dependence on a foreign currency
brought with it high production costs due to inflation, making it impossible for the sector
to develop sufficiently to supply local demand and to export in competition with the
surrounding countries.
With respect to the identification of the factors affecting rice export levels between
1980-2020, production cost, exchange rate, international price, price per bag and
inflation were identified as independent variables that in one way or another have a
negative or positive impact on exports per ton.
Through correlation, multiple linear regression and ordinary least square, it was possible
to evaluate the relationship between the levels of rice exports and the factors that affect
it, during the years 1980-2020; through which it was determined that there is a positive
correlation between the export per ton of rice with the international price and the cost
of production; while the negative values obtained for the independent variables:
exchange rate, price per bag and inflation show that there are no statistically significant
correlations. Also, the t-test determined that there was a significant change in rice
exports before and during dollarization.
REFERENCES
Dávalos, M. (2004). La dolarización en Ecuador: Ensayo y crisis. Quito: Abya-Yala.
De Bernardi, L. (February 28, 2022). Rice market profile. Retrieved from Ministerio de Economía Argentina:
https://www.magyp.gob.ar/sitio/areas/ss_mercados_agropecuarios/areas/regionales/_archivos/000
030_Informes/000020_Arroz/000021_Perfil%20del%20Arroz%20-%202017.pdf
Fiallo, J. (2017). Importance of the agricultural sector in a dollarized economy. Retrieved from Repositorio
Universidad San Francisco de Quito.
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Lascano, E. (2018). Exports as a fundamental element of economic generation. Retrieved from Repositorio de
la Universidad de Guayaquil.
MAGAP. (2013). Plan de mejora competitiva y hoja de ruta de la cadena agroindustrial del arroz. Quito:
MAGAP.
Zambrano, R., San Andrés, P., & Paredes, I. (2019). Factors affecting exports of SMEs in Ecuador. Period
2012-2016. Revista Espacios, 40(40), 4.