Journal of Economics, Finance and Business Analytics
2024; 2(2): 1- 9
https://quantresearchpublishing.com/index.php/jefba/article/view/29/version/30
DOI: https://doi.org/10.17613/3cta-n869
ISSN:3006-0745 (Online)
The nexus between credit access and agricultural
productivity in Sub-Saharan Africa: A Systematic Review
and Meta-analysis
Tarekegn Tadewos 1* and Berhanu Kuma2
1,2 Department of Agricultural Economics, College of Agriculture, Wolaita Sodo University, Wolaita Sodo, Ethiopia
Email Adress:
tarekegtadewos74@gmail.com (Tarekegn Tadewos), berhanu.kuma@wsu.edu.et (Berhanu Kuma)
*Corresponding Author
To cite this article:
Tadewos, T.& Kuma, B. (2024). The nexus between credit access and agricultural productivity in Sub-Saharan Africa: A Systematic Review
and Meta-analysis. Journal of Economics, Finance and Business Analytics, 2 (2), 1-9
https://quantresearchpublishing.com/index.php/jefba/article/view/29
Received: 19 February, 2024; Accepted: 25 February, 2024; Published: 29 February, 2024
Abstract: Many Sub-Saharan African economies heavily rely on agriculture, which has been under several shocks and
consequently has resulted in low productivity that has caused food insecurity. In particular, the nexus between credit access
and agricultural productivity, however, is still poorly understood. This paper carried out Preferred Reporting Items for
Systematic Reviews and Meta-Analyses on 26 carefully chosen published items and estimated the overall effects of credit
access on agricultural productivity in sub-Saharan Africa. The use of PRISMA 2020 criteria is where our study breaks. The
search strategy to identify relevant articles for the review was done on online scholarly databases Google Scholar, Science
Direct, Africa wide Knowledge, and the AGRIS open database, through
-Saharan Africa
random effect model and meta regression analysis were applied to show the effect of credit access on agricultural productivity.
The model output showed that access to credit increases agricultural productivity adoption by 0.96 unit, as compared to
farmers who have no access to credit. The meta-analysis suggests that agriculture is more exposed to credit constraints than
other (non-agricultural) sectors, ceteris paribus, with an overall pooled association effect size for credit access and agricultural
productivity of 0.96 (95% CI, 0.842 = 83%, P < 0.000).Future
reviews and meta-analyses increasingly focusing on methodological details are recommended to provide insights on credit
access effects on sub-Saharan African agricultural productivity, which is mainly responsible for food security in the region.
Policy implications and prolonged credit effects on agricultural productivity in sub-Saharan Africa are then contemplated.
Keywords: Credit access, Agricultural productivity, Meta-analysis, Systematic review, and Sub-Saharan Africa.
1. Introduction
Many economies in Africa heavily rely on agriculture, but agriculture has suffered several shocks that resulted in lower
production and raised concerns about food insecurity. In Africa as a whole 239 million people (17.8% of the total) experienced
chronic hunger in 2019, and 399 million extra (29.5%) had extreme food insecurity (FAO., 2022). Although Sub-Saharan
Africa (SSA) currently has an average yield from agriculture that is roughly 50% lower than that of other nations with middle
or low incomes globally, major increases in farm productivity are expected to combat hunger as well as poverty, and less than
20% of their biological potential is realized in average yields (www.yieldgap.org) (Stevenson et al., 2019).
More than half of rural people in Sub-Saharan Africa rely on agriculture for their farm incomes, food, and informal
2 Tadewos & Kuma: The nexus between credit access and agricultural productivity in Sub-Saharan Africa: Meta-analysis.
employment, which accounts for a sizeable portion of their GDP (Dorward & Chirwa, 2010). According to (Chirwa &
Dorward, 2013), there is a belief that rural populations can achieve higher incomes and reduce hunger by enhancing the
production potential per land unit, providing access to credit, and ready markets. Nevertheless, this calls for greater funding for
R&D as well as the application of innovative, farmer-friendly agricultural technologies, all of which boost output, guarantee
food security, and yield higher returns on investment(Adamu, 2022).
Using technology innovations, implementing targeted investments, and maintaining continuous production with higher
efficiency are all necessary for output from agriculture, as account for one of the basics of growth in the economy (Ter n et al.,
2014). For agricultural producers to purchase farm inputs among other things, formal agricultural credit is important (Rehman
et al., 2019). There can occasionally be a delay between the money farmers spend on raising livestock and/or growing crops
and the money they get from marketing their produce. For farmers, having access to agricultural credit is especially crucial
during this transitional period (Taremwa et al., 2021).
Lack of official agricultural credit may force many agricultural operations to become unprofitable due to excessively high-
interest rates and unfavorable terms when borrowing from unofficial sources like moneylenders, friends, and family (Seidu &
Tanko, 2022). Like other firms, agriculture needs cash for both operations on the farm and the purchase of contemporary
agricultural machinery. When cash is made available on time, modern technologies, and seeds are adopted, increasing farm
efficiency and output, which in turn accelerates growth (Kassie et al., 2013; Mazvimavi & Twomlow, 2009). Credit is one of
the main obstacles preventing small-scale farmers from implementing agricultural technologies and becoming productive
(Flory, 2012). The uptake of technology by agriculturalists and their overall potential for output are limited in the absence of
formal credit options for marginal and smallholder farmers. The provision of credit services allows the majority of farmers in
Sub-Saharan Africa, who are impoverished and among the most destitute of the poor, to escape poverty and maintain their
standard of living.(Abdallah et al., 2019).
However, there exist conflicting results for studies conducted in the sub-Saharan region of Africa that demonstrate the
adverse effects of credit on smallholder producers in agriculture (Mboulou, 2020; Siyoum et al., 2012). The present
investigation was intended to resolve issues among several divergent researchers and improve the accuracy of findings in light
of those inconsistent results. Therefore, this paper responded to the following main question: i) what is the overall effect of
credit access on the agricultural productivity of smallholder farmers in Sub-Saharan Africa? To respond to the question and
come up with policy relevant information, quantitative analysis (meta-analysis) was conducted. Moreover, the theory suggests
that smallholder farmers' agricultural credit access decisions depend on complex interactions among a large set of factors
including demographics, wealth, agroecology, markets, information, social networks, risk, and uncertainty(Pannell & Claassen,
2020). The inability of empirical results to converge on the main factors influencing agricultural productivity is partially
attributable to this complexity. The majority of individual studies typically present peculiar findings that are unique to a certain
farmer group, productivity, technology, or geographic area (de Oca Munguia & Llewellyn, 2020; Prokopy et al., 2019).
Review and synthesis papers have increased as a result of the growing demand in this field for evidence-based
policymaking(de Oca Munguia & Llewellyn, 2020; Ruzzante et al., 2021). Thus, this study summarizes findings from about 23
years of published work regarding the nexus between agricultural productivity in Sub-Saharan Africa and credit access. With
the help of these guiding principles of agricultural efficacy, policymakers, researchers, and lenders will be able to focus on
enhancing the quality of financing for smallholders in Sub-Saharan Africa.
.
2. Methods and Materials
2.1 Study Protocol
A database of applied economics for agriculture publications concerning access to credit in sub-Saharan Africa was
developed by drawing on the work of (Rosenstock et al., 2015). In this investigation, methods for locating research on the
relationship between credit and productivity in agriculture have been developed using similar search terms. Using the
selection criteria, articles that meet the study's goal were found. The desired outcome was established before the beginning of
article identification. Consequently, studies on sub-Saharan Africa's productivity in agriculture and credit services were
carefully chosen for this investigation. All of the following had been necessary for inclusion: English-language manuscripts;
published articles; dissertations; studies that focused on sub-Saharan Africa; studies looking at the relationship between credit
access and agricultural productivity or credit availability and farming; cross-sectional studies; and studies done between 2000
and 2023. However, book chapters, reviews, research published before 2000, studies performed elsewhere than sub-Saharan
Africa, studies involving both time series and panels of data, abstract-only papers, dummy credit variables, and replicate
articles were excluded. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)
guidelines for systematic evaluation procedures guaranteed the methodology and design of this study.
Journal of Economics, Finance and Business Analytics 2024; 2(2): 1-9 3
2.2 Data Search Strategies
The article search strategy was leveraged by the overall objective, namely to summarize the currently documented access to
credit nexus on the agricultural productivity in sub-Saharan Africa. The search to identify relevant articles for the review was
done on online scholarly databases (Google Scholar, Science Direct, Africa wide Knowledge (multidisciplinary index to
research and publications by Africans and about Africa), and the (International System for Agricultural Science
and Technology) open database, through the use of keywords and search terms. To increase the chances of hitting, more
articles were included through thorough referencing and joining together search terms logically returns good results. Therefore,
data searching was done systematically using Google Scholar, Science Direct, Africa Wide Knowledge, and AGRIS
Open database by using key terms. Furthermore, additional data were also procured through referencing and author sharing.
Key terms like Credit access AND Agricultural Production OR Agricu
- were used to get pertinent articles that could respond to the review questions. The database search
was conducted from June 02, 2023, to October 05, 2023.
2.3 Data extraction method
To ensure data accuracy and consistency, pertinent study data was extracted using the standardized extraction form. Finding
pertinent articles for the study is the main focus of the data extraction method. The use of the EndNote library allowed us to
remove all duplicate articles from the title and the summary, the first reading phase allowed us to remove some selected articles.
Then after a full reading of the articles, studies that met the inclusion/exclusion criteria were selected. Studies were extracted
based on; study design, study area, year of publication, research target, and outcome. The data were extracted for all
agricultural productivities such as crop-related productivities, livestock productivities, and other agricultural productivities,
which were solely done in different regions of Sub-Saharan Africa. After the data were extracted, they were reported in an
aggregated form as a case. From reviewed articles data were created in Excel and organized to see the average effect size of
credit access on agricultural productivity in Sub-Saharan Africa. The studies were also grouped by regions into SSA sub-
regions (West Africa, East Africa, Southern Africa, and Central Africa) based on the United Nations (UN) scheme classification
and by year of publication. However, no study from central Africa met the inclusion/exclusion criteria for this specific study.
2.4 Quality Assessment
When researchers use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist and its
extensions, it guarantees that they generate thorough, superior research reviews. It also facilitates a more seamless professional
evaluation process(Page et al., 2021). PRISMA guidelines were therefore used to evaluate the quality of the articles that were
being reviewed. All of the selected research studies belonged to the same category, and each analysis complied with the
rigorous standards established by PRISMA, 2020.
2.5 Statistical analysis
Data analysis was done using STATA version 16 software. In this study, both qualitative and quantitative data analyses were
undertaken. Frequency and percentage measures were applied to analyze qualitative data. In this study, credit access was used
as an intervention variable, whereas agricultural productivity was used as a response (outcome) variable. In this study, credit
access continuous mean effect values were and used as two group comparisons (i.e the overall mean effect value on credit
accessed(treated) and not accessed(controlled) See forest plot of STATA 16 output below) of mean values and agricultural
productivity was used as the continuous outcome variable thus dummy measures for these variables were not considered in this
study to keep methodological similarity. The random effect, fixed effect, and combined effect models are the three models that
can be used in a meta-analysis. Using the heterogeneity between the selected studies as a basis, the model is chosen. (Higgins
& Leturque, 2010).
In a meta-analysis, heterogeneity can arise from variations in the impact of the intervention variable on the outcome variable,
which may be attributed to variations in product type, target group, geography, or other factors. I2 was used to measure
heterogeneity (I2 = 0, no heterogeneity; I2 = 050%, low; I2 = 50%75%, medium; and I2 = > 75%, high heterogeneity). When
there is a considerable amount of heterogeneity, a random effect model is used; when there is little heterogeneity, a fixed effect
or combined effect model is used(Fletschner et al., 2010). In this study random effect model was applied since there is strong
heterogeneity (I2 = 83%)
observations. Moreover, meta-regression analysis was conducted using the Meta effect size as the dependent variable with
some modifications in moderators. A publication bias test was also performed using the regression-based Egger test. If p <
0.05 for the biased coefficient, it showed the presence of publication bias and the insignificant test result denoted the absence
of publication bias (Dong et al., 2012).
3. Results and Discussions
3.1 Articles identification
4 Tadewos & Kuma: The nexus between credit access and agricultural productivity in Sub-Saharan Africa: Meta-analysis.
We identified a total of 18,268 original articles in four databases, referencing studies and sharing from authors. By using
EndNote 20, after the deletion of the 8,804 duplicates, 9,464 articles were retained. A title-based selection resulted in the
exclusion of 3,975 articles that did not meet the specific criteria. Abstracts of the remaining 5,489 articles were read and
reviewed, excluding 5,457 more articles. After reading the full text of the remaining 32 articles, 6 more articles were excluded.
Thus, only 26 articles meeting the inclusion criteria were selected. The representative schema of the research and the number
of eligible studies are shown in Figure 1 by applying the PRISMA ,2020 criteria (Page et al., 2021). The PRISMA Report
consists of a 27-item specification and a four- (Agbodji & Johnson, 2021; Girma & Kuma, 2022).
Moreover, the final list of studies used for doing a meta-analysis is shown in Table 1.
By EndNote 20
Fig. 1. Flowchart for the selection of studies based on the PRISMA 2020 guidelines.
Data base searching:
Google scholar (n=17,800)
Science direct (n=315) and
Africa wide knowledge (n = 51)
2. Additional records identified through reference of
studies (n= 7)
Shared by authors (n= 2)
3. Removed Duplicates
(n = 8,804)
4. Records screened
(Titles and abstracts review)
n= 9,464)
5. Records Excluded
(Not met with the specific study criteria)
(n=3,975)
6. Full-text articles assessed for
eligibility
(n= 5,489)
7. Full- text articles excluded with reason
(n= 5,457)
Full-text not met criteria (n= 6)
Encyclopedia (n = 48)
Books (n=438)
Review articles (n= 1,213)
Not study (n= 1,347)
Study done outside SSA (n= 2210)
Abstract only (n=201)
8. Studies included in qualitative synthesis
(n= 26)
9. Studies included in quantitative synthesis (Meta-
analysis)
(n= 26)
Identification
Screened
Eligibility
Included
Journal of Economics, Finance and Business Analytics 2024; 2(2): 1-9 5
Table 1: The final identified studies for doing a meta-analysis
No.
Author
Publication
year
Study area
(Place)
Research target
Sample
Size
Research
Design
1
Gebeyehu et al.
2020
Ethiopia
credit access & farm productivity
260
cross-sectional
2
Geleta et al.
2018
Ethiopia
credit impact on HH income
188
cross-sectional
3
Berhanu et al.
2021
Ethiopia
credit impact on HH food insecurity
360
cross-sectional
4
Adamu
2022
Ethiopia
credit access & farm productivity
376
cross-sectional
5
Mengistu
2019
Ethiopia
Institutional credit & crop productivity
853
cross-sectional
6
Geta & Hamiso
2017
Ethiopia
microfinance credit & crop productivity
194
cross-sectional
7
Awe
2020
Nigeria
credit access & farm output
94
cross-sectional
8
Ponguane
2016
Mozambique
credit subsidy on crop productivity
107
cross-sectional
9
VUKEY
2019
Ghana
savings & agricultural productivity
222
cross-sectional
10
Taremwa et al
2021
RWANDA
credit access & agricultural productivity
422
cross-sectional
11
Kehinde
2021
Nigeria
Agricultural credit on productivity
300
cross-sectional
12
Nosiru
2010
Nigeria
Microcredit & agricultural productivity
90
cross-sectional
13
Olagunju & Babatunde
2011
Nigeria
credit& poultry productivity
280
cross-sectional
14
Seidu & Tanko
2022
Ghana
credit program & Maize productivity
130
cross-sectional
15
Bakare et al.
2023
Nigeria
Microcredit & rice productivity
320
cross-sectional
16
Awotide
2015
Nigeria
credit & agricultural productivity
856
cross-sectional
17
Alfa & Abdulfatah
2019
Nigeria
credit & rice productivity
389
cross-sectional
18
Ojo
2019
Nigeria
credit & rice productivity
360
cross-sectional
19
Spio
2006
South Africa
credit and agricultural productivity
153
cross-sectional
20
Njogu et al.
2018
Kenya
Bank credit and agricultural production
316
cross-sectional
21
Assouto & Houngbeme
2023
Benin
credit & agricultural productivity
356
cross-sectional
22
Motsoari
2012
Lesotho
credit & agricultural productivity
100
cross-sectional
23
Agboklou & Ozkan
2023
Togo
credit & rice farm productivity
102
cross-sectional
24
Girabi & Mwakaje
2013
Tanzania
microfinance credit & farm productivity
98
cross-sectional
25
Ibrahim & Bauer
2013
Sudan
credit & farmers' profitability
200
cross-sectional
26
Ogundeji et al.
2018
Lesotho
credit & farm income
100
cross-sectional
3.2 Results from the Meta-Analysis
This subsection portrays the results of the descriptive statistics and Meta-analysis, framed around the economic effects of
credit access on agricultural productivity in Sub-Saharan Africa. Recall that 26 articles retained for the systematic review
(100%) qualified for the Meta-analysis. Almost all of the retained articles here are empirical, country-specific articles, probably
because they contained same-scale data on agricultural sectors, which was a prerequisite for the meta-analysis (Gerstner et al.,
2017).
3.3.1 Credit Access and Agricultural Productivity Studies by Regions in Sub-Saharan Africa
The horizontal bar graph in Fig. 2 below portrays the percentage of studies done on the Connection between credit
availability and agricultural productivity in each region of SSA except central Africa, where no study meets the specific criteria
of this study. Fig. 2 below also, shows that about 46.1% of studies were done in the western African region, 38.5% of studies in
the eastern African region, and 15.4% of studies in southern Africa. This suggests that there aren't enough studies on how credit
availability and agricultural productivity are related, especially in the southern and central regions of SSA. Therefore, to
improve agricultural output across the region and make the best use of the resources that are available for credit access, it is
necessary to carry out concrete investigations. This is consistent with the outcomes of a systematic review and meta-analysis in
Ethiopia (Girma, 2022), credit access can help smooth out supply constraints for agricultural inputs. Therefore, studies must
take due consideration on the relationship between agricultural productivity and credit in SSA.
Fig. 2 Credit access and agricultural productivity studies by region in Sub-Saharan Africa.
6 Tadewos & Kuma: The nexus between credit access and agricultural productivity in Sub-Saharan Africa: Meta-analysis.
3.3.2 The effect of Access to Credit on Agricultural productivity in Sub-Saharan Africa
The findings of the random effect model's meta-analysis regarding the effects of credit availability on
agricultural productivity in Sub-Saharan Africa are shown in Fig. 3 below. Hedge's g statistics were used
to illustrate the average effect size.
Fig. 3 Random effect model output
According to the model's output, which is based on the above figure, producers who have access to credit are 0.96 units
more productive overall than those who do not. The argument was that in the SSA, gaining access to credit increases farmers'
economic and agricultural productivity and enables them to supply, buy, and use contemporary farm inputs. This result is
consistent with the research (Adamu, 2022; Nordjo & Adjasi, 2020; Taremwa et al., 2021; Zewdie, 2015), that demonstrated
how having access to credit enhances farmers' ability to supply and buy modern farm inputs and technology, which increases
both the amount and the quality of production. Furthermore, an I2 value of 83% with p < 0.000 was found in the model output,
falling within the range of significant heterogeneity. This suggested that the impact of credit availability on farmers' overall
agricultural productivity in Sub-Saharan Africa varies (heterogeneously). This result is consistent with (Thompson, 2001).
3.4 Meta-Regression Analysis
A statistical method for estimating the mean and variance of actual demographic effects from a group of empirical analyses
purporting to address the same topic of study is called a meta-analysis. Additionally, as (Egger et al., 1997), the slope is
derived from a weighted regression of the effect size (Meta es), on its standard error (Meta se), with the option to adjust for
moderators. The result from Table 2 showed a tau square value of 8.5e-07 and a smaller I-squared residual value of 0.00%
which implied that all covariates have explained almost all of the variation between the studies. The joint test for all of the
covariates gives a p-value of 0.0000 indicating some evidence for an association of at least one of the covariates with the
treatment effect. Furthermore, the regression analysis showed an adjusted R-square value of 100% which implied the between-
study variance explained by all covariates for these case-eligible studies. The impact of credit access on overall agricultural
productivity is as expected showing positive coefficients in the treated group (credit-accessed households) and negative
Journal of Economics, Finance and Business Analytics 2024; 2(2): 1-9 7
coefficients in the controlled group (not credit-accessed households). This is in line with the findings of (Bakare et al., 2023;
Geleta et al., 2018; Gershon et al., 2020).
Table 2: Meta-Regression Analysis Output.
3.5 Publication Bias Diagnostic Test
The funnel plot, a widely used diagnostic visualization in meta-analyses, is particularly advantageous to evaluate small-
study effects and bias in publications(Light & Pillemer, 1984). It is a case study of a scatter diagram used to compare results
from studies, which shows the proximity of the estimated intervention effect size to the true impact measurement. Graphical
evaluation of funnel maps can be subjective when detecting imbalances, but it is still valuable for data exploration (Peters et al.,
2006). It is desirable to evaluate funnel-plot asymmetry more formally. Thus, Statistical methods were developed to identify
the asymmetry in a funnel diagram(Sterne et al., 2011). To quantify funnel graphs, Egger's regression statistical test for small
study effect was employed. This test offers weighted regression analyses of the magnitude of the effect forecasts on their
precision measures (standard errors). In addition, Egger's regression statistical test is recommended for grouped or ungrouped
continuous data in comparison to Begg, Peters, and Harbord tests. Bias in publication can be caused by chance, valid
differences, reporting bias, and poor methodological quality (Egger et al., 1997). The intercept line is the relevant metric in this
case. A statistically significant intercept, or one having p < 0.05, indicates biased publication. As indicated in Table 3, the test
statistics for this study's results indicated bias in publication was not present.
Table 3: shows the regression-based Egger test for small study effects
Test of H0: no small-study effects P = 0.8828.
The Egger's test result in Table 3 showed a bias coefficient of 0.15 and a standard error of 1.036, giving a p-value of
0.8828. This test thus suggests little evidence for the presence of small-study effects. Therefore, smaller studies (those with
Therefore, the study
result from the test statistics showed the absence of publication bias.
Test of residual homogeneity: Q_res = chi2(21) = 0.00 Prob > Q_res = 1.0000
_cons 1.11e-16 .227787 0.00 1.000 -.4464543 .4464543
Eventcotrolled -2.91e-19 .0004484 -0.00 1.000 -.0008788 .0008788
EventTreated 1.81e-19 .0005923 0.00 1.000 -.0011608 .0011608
_meta_se -5.20e-16 1.256683 -0.00 1.000 -2.463053 2.463053
_meta_es 1 .08642 11.57 0.000 .8306198 1.16938
_meta_es Coef. Std. Err. z P>|z| [95% Conf. Interval]
Prob > chi2 = 0.0000
Wald chi2(4) = 157.83
R-squared (%) = 100.00
H2 = 1.00
I2 (%) = 0.00
tau2 = 8.5e-07
Method: REML Residual heterogeneity:
Random-effects meta-regression Number of obs = 26
Prob > |z| = 0.8828
z = -0.15
SE of beta1 = 1.036
beta1 = -0.15
H0: beta1 = 0; no small-study effects
Method: REML
Random-effects model
Regression-based Egger test for small-study effects
8 Tadewos & Kuma: The nexus between credit access and agricultural productivity in Sub-Saharan Africa: Meta-analysis.
4. Conclusions
Agriculture is constantly subject to a variety of unpredictable factors, including changes in the climate, financial constraints,
and man-made and natural hazards. Credit access is crucial for enhancing agrarians' ability to smooth out input supply
constraints and sparking demand for heightened output in farming. The results of the random effect model show that if sub-
Saharan African farmers have access to credit, they can raise agricultural productivity by 0.96 (95% CI, 0.841.09; P = 0.00)
units. Agricultural chemicals, livestock, high-producing plants, and other productive inputs could be purchased by farmers with
easier access to credit. Based on the results of this systematic review and meta-analysis, Productivity in agriculture is
significantly enhanced by the availability of credit in Sub-Saharan Africa. Thus, governments at various levels and non-
governmental organizations supporting farmers in SSA countries should give due attention in enabling farmers to access credit
to boost agricultural productivity. They should give top priority to establishing rural financial institutions that operate jointly
with the private sector to provide credit and savings services to the farming community. This can be possible through investing
in rural infrastructure, agricultural input supplies, technologies, and the establishment of centers that educate and train farmers,
all of which are crucial in raising farmers' awareness and enhancing their use of credit. This credit service should be provided
at low-interest rate to make it available to all farmers.
Conflicts of Interest
The authors declare no conflicts of interest.
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