A
Demographic Determinants of private investment in the pastoral and agro pastoral communities In Afar regional state, EthiopiaAbstract
Political and economic activities have neglected the Afar regional state and its community, limiting the development potential of this pastoral and agropastoral community. To address this, the Afar Regional State Investment Commission was established to promote and facilitate regional investment by fostering a favorable investment climate. Nevertheless, private investment in the region is still at an unsatisfactory stage. So, an attempt is made to provide some insight on demographic determinants on private investment growth in the Afar regional state, Ethiopia, the study is employed mixed research approach and utilized data collected from 309 private investors in the region by using cross-sectional data analyzed through descriptive statistics and a binary logistic model in Stata 14 software. Gender, educational attainment, age, marital status, ethnicity, culture, and the number of dependents are the main demographic variables that are considered in this study. The study reveals that gender and age positively and statistically significantly influence private investment growth. While the number of dependents has a negative and significant influence on private investment growth in the region. The research contributes practical policy-making direction to the Afar Investment Commission by shedding light on how demographic factors are shaping private investment growth and offering critical direction development efforts in the region. This study examines the influence of demographic determinants of private investment in the Afar regional state. this area has received little attention in research, and the study contributes unique insights into the determinants influencing investment in a neglected region of Ethiopia.
Keywords:
private investment
cross-sectional
Afar Regional State
binary logit
Afar Investment Commission
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Introduction
Economic growth is the common priority for each society (Mankuroane, 2021). Above all, today, the world can broadly be divided into developed and developing nations, and this distinction is the impact pattern of investment (Dr. Megha Sharma & Dr. Devendra Lodha, 2024). In most sub-Saharan African countries, economic stagnation still prevails or has demonstrated little growth. The result is high capital cost, minimal income, and rising poverty levels in this region. Investments will be crucial for the substantive growth mentioned above due to the income benefits they mercifully bestow on the social system (Lotto, 2023). Private investment canonically provides an opportunity for development and expansion of the economy in all developing countries. Nevertheless, private investment within developing countries remains quite low, particularly among low-income ones. (Ayeni, 2020).
Demographic determinants play a significant role in shaping private behaviors and decisions. Age and Gender are critical in determining risk preference and investment behaviors (Ezekiel & Prince Oshoke, 2020). Higher educational levels and professional occupations are associated with a greater propensity to invest and a higher affinity for risk(Geetha & Ramesh, 2012) According to, (Ghosh, 2022), Individuals with larger families prioritize stability and security over high-risk investments.
The Afar region is predominantly inhabited by Afar ethnic groups, which have their cultural identity and tradition influencing their economic activities. Moreover, the region has the lowest literacy rates and gender imbalance in economic activities, and female unemployment rates are higher than males (Goshu et al., 2021). A low private rate of capital formation underscores private investment in Afar. Literature on the private investment determinants of income and savings is not directly useful in analyzing private investment in a pastoral setting because of the unpredictable nature of pastoral income flows and the uncertainty of the economic environment.
Based on the literature surveys conducted, the following findings were identified, which helped to indicate the existing gaps in the relevant literature. Furthermore, there is inconsistency in the review of earlier research on the factors influencing private investment in Ethiopia. Therefore, the existing findings of the study are not consistent, especially regarding region-related demands. Similarly, the results also show that demographic factors are highly important in attracting investment in the region as compared to income and other factors. This manuscript fills the previous gaps and provides information about the differential impact of demographic dynamics on investment destinations. In other words, the study adds value to the existing literature by examining the demographic determinants of the location of private investment in the Afar regional state of Ethiopia.
The study aimed to find demographic variables influencing private investment in the Afar Regional State, Ethiopia. Specifically, it attempted to sufficiently tackle the overlooked issues concerning private investment in a pastoralist society such as the Afar Regional State, Ethiopia.
Literature review of the study
Economic growth and development are significantly influenced by private investment. besides that, private investment and demographic factors are closely interrelated, according to many studies that indicate that demographic factors influence economic growth, investment decisions either to save or invest, and organizational dynamics. Understanding private investors' demographic characteristics is pivotal for policymakers, financial institutions, and business owners to make informed decisions. Therefore, based on the existing research on demographic factors such as age, gender, income, education, and occupation. By synthesizing the findings from previous studies.
Global level
Several scholars, including (Ghosh, 2022), (Mehta & Aggarwal, 2011), (Menaga, 2024), (Mishra, 2023)Mishra, (Patel & Modi, 2017), (Perera & Gunathilaka, 2023), (Salim & Setyawan, 2023), (Wenjin & Sahid, 2023), and (Xie et al., 2024) have investigated the influence of demographic factors on private investment from various perspectives, emphasizing diverse focal points in their research.
In the case of Turkey, Doğaner et al. (2024) conducted a study on the impact of stock market literacy on individual investors' investment decisions, employing a robust methodological approach. The study observed that greater stock market financial literacy positively correlated with individual investors' investing decisions in the study area. Although the preceding finding was confirmed by (Kumar & Manchem, 2024), investors with higher financial literacy are better at making educated investment decisions, resulting in well-managed portfolios and increased wealth over time. In contrast, (Senda et al., 2020) discovered that financial literacy does not affect investing decisions. This study also found that demographic characteristics of investors such as age, income, and investment experience have positive and direct effects on investment decisions. Meanwhile, other demographic factors like gender and educational level of investors do not affect individual investors' investment decisions.
A related study, (Amutha, 2014) examined the effect of demographics on investors' investment choices in Chania City, India. The study employed a quantitative descriptive design. The data was collected in March 2013 with the use of a questionnaire and distributed to 300 respondents who were selected randomly from Chennai city. Out of 300 respondents, 283 responses were considered in the study. The study found that age, income, and education influence investment choices among investors in India, highlighting significant variations in investment avenues before and after economic liberalization, reflecting changing consumer lifestyles and preferences. (Patel & Modi, 2017)) carried out an empirical study examining the influence of demographic factors on investment decisions in the South Gujarat region. Their findings indicate that demographic characteristics, including age, gender, and income, significantly affect investment decisions. A related study conducted in Sri Lanka (Subramaniam & Velnampy, 2017) explored the impact of demographic factors on private investment performance. They similarly concluded that demographic variables have a notable effect, although they found that marital status does not play a significant role in influencing private investment performance.
Another related study, (Danila et al., 2019) on socio-demographic characteristics on investment objectives of individual investors; an empirical study in Indonesia. The study found that age, gender, level of education, and marital status have significant effects on the investment objective of individual investors in the Malang district.
The recent study explores various demographic factors that influence the investment decisions of individual investors, particularly focusing on age, sex, marital status, and educational level. The study indicates that age, sex, marital status, and educational level have a significant impact on private-sector investment behavior (Nguyen et al., 2024). This finding is consistent with a similar study conducted across the continent. (Asiedu & Freeman, 2009)
Africa level
Furthermore, Empirical studies by (Oppong et al. 2023) examine the nexus between financial literacy, investment decisions, and personal financial management among private-sector employees in Ghana. The study used a quantitative research approach using a structured questionnaire with close-ended questions and data collected from 400 employees selected through convenience sampling. The study adapted Partial Least Squares Structural Equation Modelling (PLS-SEM) to evaluate the relationships among the variables. Key findings reveal that financial literacy positively impacts both investment decisions and personal financial management. Additionally, investment decisions mediate the relationship between financial literacy and personal financial management, indicating that improving financial literacy enhances personal financial management outcomes through better investment decisions. The study emphasizes the necessity of financial literacy training to improve financial management and investment behaviors among employees.
The study on the association between South African investors' financial risk tolerance and demographic factors was conducted by (Maritz & Oberholzer, 2019) The study was conducted using both descriptive and statistics models and found that men have statistically significantly more risk tolerance than women, income level and combined income, and educational level of investors have all positive associations and are statistically significant with the risk tolerance of individual investors. The study also reveals that older investors have more risk tolerance than younger ones, married investors are more risk-tolerant than unmarried investors, and the number of dependants is positively associated with risk tolerance.
Sub-Sahara Africa level
According to (Bosire et al., 2018) in a study on the effects of demographic factors on the value of investment teachers in Kisii County, Kenya. The study used data collected from 313 public secondary school teachers in Kisii County, and inherited investments were not considered in this study. The data used ordered logistic regression. Demographic factors such as age, gender, and income were found to have positive and significant effects. On the other side, marital status, number of dependents, financial training, education, length of service, and religion were found to be insignificant.
Another study conducted by (Nasage, 2019) on the same study, the Upper West Region of Ghana. The researcher applied a mixed research approach, and 300 respondents were selected using convenience sampling where a snowball was used from the municipality of WA. The SPSS version 25 software was used for the data analysis. The study indicates that demographic factors, such as age, sex, marital status, experience, and income level, significantly influence individual investment decisions, meanwhile educational level weakly indicates investment decisions in WA Municipality Ghana, suggesting similar trends may exist in other African contexts regarding private investment. (Willows & West, 2015) The study indicates that younger investors hold more volatile portfolios, while older investors exhibit lower variances in returns. Behavioural biases differ by age, with younger individuals showing higher trading frequency and older individuals demonstrating more stable investment behaviors.
age significantly influences investment behavior in Sub-Saharan Africa, with younger individuals prioritizing savings accounts, while older individuals focus on stocks and land, reflecting a shift in investment goals and asset allocation across the lifecycle.(Karanja, 2019)
Ethiopia level
The effect of demographic determinants on private investment is not studied much at the Ethiopian level. Nevertheless, there are very few studies related to demographic determinants. According to (Wubie et al., 2015) studied the influence of demographic factors on saving and investment of high school teachers in Ethiopia; case study on the Dangila district. The study used primary data from the structured questionnaire from 88 high school teachers in Dangila district selected through a simple random sampling technique. The study adopted a multiple linear regression model to the effects of 8 explanatory variables on the dependent variable (i.e., saving and investment), and the study found that 4 demographic factors (i.e., Gender, Age, Family size, and social ceremony expenses) have a significant influence on Saving and investment.
Another study conducted in the east part of Ethiopia by (Aklilu, 2021) the study was on determinants of private investment in Dire Dawa City. Like the above study, the researcher used primary data from a self-administrative questionnaire through systematic sampling and analysed it by inferential analysis and developed a logistic regression model. The study employed SPSS for the data analysis, and the result of the study reveals that nine explanatory variables such as education, marital status, age, personal saving, inflation, public investment, investment incentive, Raw material, and land are statistically significant factors that determine private investment in dire Dawa city. In contrast to (Aklilu, 2021);The study conducted in the Woliata zone, southern Ethiopia, found that age, sex, and education are statistically significant and negatively correlated with investment performance (Moges et al., 2022). demographic factors are crucial for understanding the characteristics of private investors in Tigray and may influence investment decisions and strategies in the manufacturing sector (Gizachew Yirtaw G., 2017).
Hypothesis of the study
After conducting an intensive review of the existing literature, the following hypothesis is developed to test the demographic determinants' influence on private investment growth in the Afar Regional State, Ethiopia.
H1: There is a significant relationship between private investment Growth and the Gender of individual investors
H2: There is a significant relationship between private investment Growth and the educational level of individual investors
H3: There is a significant relationship between private investment Growth and the age of individual investors
H4: There is a significant relationship between private investment Growth and the marital status of individual investors
H5: There is a significant relationship between private investment Growth and the Ethnicity of individual investors
H6: There is a significant relationship between private investment Growth and the culture of individual investors
H7: There is a significant relationship between private investment Growth and the Number of dependents of individual investors
Conceptual framework of the study
The conceptual framework is a representation of all the variables connected within a system of interest, depicting the connections that complete the system, thus indicating the situation briefly. Demography is the statistical study of populations, primarily humans.
This study is initiated to examine the private investment of Afar society members and to identify the effects of selected demographic determinants. These are found to affect investment in various dimensions. As a result, the major aim of this section is to review the conceptual framework and hypothesis of private investment and selected demographic determinants affecting private investment growth among investors financing in urban and rural kebeles. According to the situation in the study area, demographic determinants are interrelated to affect people’s investment growth across various dimensions among the people of the region. Gender, Age, educational level of investors, marital status, dependence ratio, ethnicity, and culture influence the growth of investment at the individual level. These factors affect private investment growth in afar regional states, either positively or negatively. The conceptual framework is shown in Fig. 1.
Fig. 1
the Conceptual Frame Work
Methodology of the study
Research Approach and Design
According to Kotheri (2004), there are three basic approaches to research. They are quantitative, qualitative, and mixed approaches. The aims of this study can be better met by using a mix of both qualitative and quantitative data. the mixed method is employed for this study. So, a mixed methods approach is useful to capture the best of both quantitative and qualitative techniques with an Exploratory type of research design method.
Sampling technique and sample size
For this study, multistage sampling strategies were used. In the 1st stage, all private investors were grouped into 3 investment sectors: Agriculture, manufacturing, and service. In the 2nd, a proportional sampling approach was used to select samples from each of these sectors based on their relative sizes. in the 3rd stage, four districts (Woredas) were purposively selected. These districts were chosen based on their high levels of investment activity and the significant number of investment licenses issued by the Afar Regional State, ensuring a focus on the most economically active areas. Finally, in the fourth stage, investors within the selected districts were chosen purposively. the sample size is determined by using the Taro Yamane formula.
formula.
--------------------------------------------- (1)
N = 1271
=309 n=309
The respondents were selected from the classified categories of investors as given below.
Categories of investment | Total of investors | Total proportionate out of 100% investors | Sample size |
|---|---|---|---|
Agriculture | 364 | 29 | 89 |
Service | 723 | 56 | 176 |
Manufacture | 184 | 15 | 45 |
Total | 1271 | 100% | 309 |
| Source: Afar Investment Commission 2024 | |||
To achieve the study's objective, primary data was used. Primary data is the collection of first-hand information using questionnaires that appropriately suit the study. Primary data were collected purposively from four districts in the Afar regional state. The questionnaire was administered by the researchers. All necessary data were collected from October to December.
Methods of Data Analysis
In this study, both descriptive and econometric models were employed. Descriptive statistics is important to have a clear picture of the characteristics of the sample units. One can compare and contrast differences. Stata 14.0 software was used for data entry, editing, and tabulation. A T-test will be used to test for the significance of continuous variables.
An econometric model was used to identify the significance of the factors affecting private investment. Based on the behaviour of the dependent variable, the binary logistic regression
Investors' decisions regarding the influence of demographic factors on private investment growth are modelled as follows:
PIGi=
(02)
The probability that private investors' investment growth is not influenced is given in Eq. 3.
pi =
(03)
The probability of private investment growth to be influenced by demographic determinants is given under Eq. (4). Where: Pi is the probability of private investment growth to be influenced by demographic determinants; βj is the coefficient to be estimated; Xi is the demographic determinant variables of private investment growth the ith investors; I is the total number of a sample of private investors (i = 1,2, 3…….309); j is the total number of determinant variable (j = 1,2, 3,…7)
04
Where:1-pi is the probability of private investment growth affcted. Now calculate the odds ratio, the odds ratio
underEq. 5.
05
If the natural log of Eq. 5 is, then obtain L is called the logit, and hence the name logit model see Eq. 6
Li = ln
= (β1 + βjXji) (06)
Logistic regression model for the private investment Growth Eq. 7
PIGi = = ln
= βo + β1GNi + β2ELi + β3Agei + β4MSi + β5CLi + β6ETHi + β7NDi (07)
Definition of variables.
Private investment growth
it is the dependent dummy variable; if investors' PIG is influenced by Demographic factors “1” and “0” if demographic factors do not influence investors' PIG.
Gender (GN)
This is an independent dummy variable and refers to the gender of the investors. “1” is a male investor, and “0” is a female investor.
Educational level (EL)
It is an independent categorical variable that represents the formal educational attainment of private investors and examines its impact on the growth of their investments. categories include “1” primary, “2” secondary, “3” diploma, “4” degrees, and “5” above degree.
Age
This is an independent variable and categorical variable that indicates the age of investors in a year. Categories include “1” very young (18–29), “2” Early Middle Age, “3” late middle age, “4” pre-retirement age (50–59), and “5” retirement Age above 60
Marital status (MS)
This is an independent and categorical variable that indicates the marital status of investors. “1 = single, 2 = married, 3 = divorced and “4 = widowed
Culture (CU) is an independent variable and dummy variable that indicates whether cultural practices influence investment growth. “1” cultural practice influences private investment growth, and “0” cultural practice does not influence private investment growth.
Ethnicity (ETH) is an independent and categorical variable that indicates the ethnic group of investors and reflects the diversity in the region. This means “1” Afar, “2” Tigray, “3” Amhara, and “4” others.
Number of dependents (NoD)
This independent continuous variable refers to the number of dependants in the investor’s household. Higher numbers indicate a greater financial burden, which may impact investment growth.
Reliability Analysis
The study questionnaire's internal consistency was assessed using Cronbach's Alpha. The recommended benchmark value for alpha is 0.7.
Table 2
Variables | Cronbach's alpha |
|---|---|
Private investment growth | 0.75 |
Gender | 0.87 |
Age | 0.73 |
Educational level | 0.84 |
Marital status | 0.79 |
Ethnicity | 0.72 |
Culture | 0.85 |
Number of Dependents | 0.89 |
Mean of alpha | 0.80 |
| Source: survey result 2024 | |
Empirical Results and Discussion
Descriptive analysis and econometric modeling were used in this study to identify the influence of demographic determinants of private investment growth in the Afar regional state. Frequency, percent, Mean, and standard deviation are used for the descriptive analysis. Before running the model, binary logit assumption tests were checked, such as the absence of multicollinearity, heteroskedasticity, and model specification tests. All those tests are satisfied. In econometrics, a binary logistic regression model was used because the dependent variable was in binary form. The results are presented as odds ratios to interpret the probability of each demographic factor on the growth.
Descriptive Statistics of Variables
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Gender: as seen in Table 2, 71.52 percent of the respondents were male. Indicates a significant gender imbalance. In the pastoral community, female participation in investment as well as economic activities is very low. because of cultural and social norms. so, this finding is not surprising, even expected. The educational level: Education plays a crucial role in private investment Growth. Table 2 indicates that Most participants (33.1%) have a degree and above, followed by primary school education (22.1). A significant percentage (21.04%) cannot read or write. The mean 3.883495 reveals that the average participant’s education level is between a college diploma and a degree and above. This insight that varying levels of education among investors. Age of the respondents: The majority of participants are in the late middle age group (40–49), comprising 40.13%. Very few are in the youngest group (1.62%). The investors in the 40–49 age group (Table 2) imply that individuals in this stage of life have the resources, experience, or motivation to invest. According to Table 2, the most common marital status among the respondents (60.84%) is that they are married, while smaller parts of the sample are single, widowed, and divorced.variable | Category | frequency | Percent | Mean | S. D | |
|---|---|---|---|---|---|---|
gender | 1 = male | 221 | 71.52 | 7152104 | .4520463 | |
0 = female | 88 | 28.48 | ||||
Educational Level | 1 = unable to read and write | 65 | 21.04 | 3.883495 | 1.928769 | |
2 = read and write | 11 | 3.56 | ||||
3 = primary school | 68 | 22.01 | ||||
4 = secondary school | 18 | 5.83 | ||||
5 = Collage diploma | 45 | 14.56 | ||||
6 = degree and above | 102 | 33.01 | ||||
Age | 1 = young (18–29) | 5 | 1.62 | 3.304207 | .9626204 | |
2 = early middle age (30–39) | 57 | 18.45 | ||||
3 = late middle age (40–49) | 124 | 40.13 | ||||
4 = pre-retirement (50–59) | 85 | 27.51 | ||||
5 = retirement Age (60+) | 38 | 12.30 | ||||
Marital status | 1 = single | 60 | 19.42 | 2.113269 | .8432998 | |
2 = married | 188 | 60.84 | ||||
3 = divorced | 27 | 8.74 | ||||
4 = widowed | 34 | 11.00 | ||||
Ethnicity | 1 = Afar | 49 | 15.86 | 2.546926 | 1.154564 | |
2 = Tigray | 86 | 27.83 | ||||
3 = Amhara | 93 | 30.10 | ||||
4 = others | 81 | 26.21 | ||||
Culture | Effects of culture on PIG | 1 = yes | 177 | 57.28 | .5728155 | .4954719 |
0 = No | 132 | 42.72 | ||||
Number of dependents | Continues variable | 2.983819 | 1.921275 | |||
| Source: survey result 2024 | ||||||
Mean 2.113269 This indicates most of them are near married. S.D. 0.8432998 indicates moderate variation. Married respondents have a more solid financial position and invest more for the sake of family security. The data in Table 2 indicates that Tigray and Amhara ethnic groups are the majority of private investors investing in the study area, according to the mean ethnicity value of 2.546926. This implies that these two ethnic groups are actively engaged in private investment, which reflects the region's inclusivity and generally harmonious cohabitation of various ethnic groups. However, afar investors' participation is noticeably very low; for a long period, afar people were marginalized from using their resources. Policymakers and the government should give special attention to this investor to increase private investment in the region. The statistical value illustrated in Table 2 is that more respondents agreed that culture affects the growth of private investment in the study area, as indicated by the mean of 0.5728155. This suggests that cultural factors play a notable role in shaping investment behaviors and decisions, potentially influencing opportunities, preferences, and resource allocation within the communities. The number of dependents in the investor's household was another independent variable. Since this variable is continuous, mean values are a better way to interpret it. According to the Table 2 statistics, a mean of 2.983819 shows that participants typically had close to three dependents. This suggests that most investors bear moderate household responsibilities.
Regression result
This paper employed binary logistic regression to assess whether investment growth is influenced by demographic determinants of private investors. Various diagnostic tests are conducted, such as a multicollinearity test, heteroscedasticity test, model specification test, and Goodness of fit test.
Multicollinearity test
Pearson correlation analysis and the variance inflation factor (VIF) were used to check for multicollinearity. According to Table 3, the mean of VIF 1.10 is significantly lower than the cut-off point 10, which suggested by(Belsley, D.A., Kuh, E. and Welsch, 1980) The VIF shown in Table multicollinearity is not the issue, based on the cut-off point.
Variables | VIF | 1/VIF |
|---|---|---|
GN | 1.08 | 0.928837 |
Age | 1.09 | 0.917884 |
EL | 1.15 | 0.871525 |
MS | 1.08 | 0.924622 |
ETH | 1.13 | 0.883095 |
CUL | 1.15 | 0.870170 |
NoD | 1.03 | 0.972483 |
Mean of VIF | 1.10 | |
| Source: survey result 2024 | ||
Heteroscedasticity test
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A Breusch-Pagan test for heteroscedasticity was used after multicollinearity was tested. This test determines if a regression model's error variance is homoskedastic or constant. One requirement to meet before performing binary logistic regression is this one. Table 4 displays the outcome of the Breusch-Pagan test. The chi-square value is above the crucial, according to the outcome. Therefore, it suggests that the hypothesis for homoskedasticity is rejected because there is no indication of heteroskedasticity in the model.Breusch-pagan/ cook-Weisberg test for Heteroskedasticity |
|---|
H0: constant variance |
Variables: fitted value of PIG |
Chi2(1) = 0.03 |
Prob > chi2 = 0.8623 |
| Source: survey result 2024 |
Model specification test.
Performing model specification tests is essential to ensure that the model is correctly conducted and provides reliable results. A link test is used to determine whether the model is properly conducted. The linear predicted value (_hat) and linear predicted value square (_hastq) must be predicted in the model. The model is correctly specified if _hat is statistically significant while _hastq is insignificant. Consistent with this, Table 5 indicates that _hat is significant and _hastq is insignificant. The model output reveals that two model specification criteria are met. So, the binary logit model is correctly specified.
PIG | Coef. | Std. Err. | t | P>|t| | [95% Conf. Interval] |
|---|---|---|---|---|---|
_hat | 1.157764 | .1954359 | 5.92 | 0.000 | .7731955 |
_hatsq | − .1555429 | .1876436 | -0.83 | 0.408 | − .524778 |
_cons | − .014564 | .0380951 | -0.38 | 0.702 | − .0895255 |
| Source: Survey Result 2024 | |||||
Binary logistic regression result
This study used a binary logistic model. Private investment growth (as the dependent variable) and seven independent variables such as gender (GN), age, educational level (EL), marital status (MS), ethnicity (ETH), culture (CUL), and number of dependents (NoD) were examined. The coefficients, odds ratios, standard Errors, Z-values, and significance levels for each variable are presented, alongside diagnostic tests for the model's overall fit and performance by using Pseudo R2.
The model output in Table 6, indicates that from seven independent variables 3 variables have a statistically significant influence on private investment growth in the study area. The remaining four variables are not statistically significant.
Gender (GN)
As shown in Table 6 private investment growth and gender have a significant and strong positive relationship, with males being 169.25 times more likely than females to experience investment growth (Z = 9.05, P < 0.001). So, males and females have different behaviors like risk-taking abilities and social norms, and this certainly influences investment growth. This finding is consistent with (A.K.Tyagi, 2024), (Wubie et al., 2015), and (Nugumanova et al., 2014). Therefore, H1 is Accepted, indicating that gender has a positive relationship with private investment growth in the study area.
Age
age plays a crucial role in our daily decision-making, particularly in the business world. The survey results in Table 6 indicate that age and private investment Growth have a positive and statistically significant relationship (Z = 2.88, P = 0.004). the coefficient for the age of 0.660, indicates that for each unit increase in investors' age, the log -odds of private investment growth increased by 0.660, and the odd ratio of 1.93 suggests that with every additional year of age, the likelihood of private investment growth increased by 93%. This implies that investors are more likely to experience investment growth. Because of greater knowledge and experience in investing and investment skills, financial stability or resources that accumulated over time. The finding is consistent with (Onsomu, 2015). So H1 is accepted, and the study concluded that older age has a positive influence on private investment growth.
Educational Level (EL)
The analysis shows in Table 6 that educational level has a positive but statistically insignificant relationship with private investment growth in the study area growth (coefficient = 0.140, odds ratio = 1.15, Z = 1.23, P = 0.218). one-unit increase in educational level is associated with a 15% increase in the likelihood of private investment growth. Therefore, educational level does not appear to be a meaningful predictor of private investment growth in the study area. Related studies found that Higher educational attainment is linked to improved economic output and growth rates(Nordhaus, 2004), (Kurt & Gumus, 2021). In contrast (Wubie et al., 2015) found that educational level is not statistically significant on private investment. Therefore, H1 is rejected.
Marital Status (MS)
The analysis indicates that marital status has a negative but statistically insignificant relationship with private investment growth (coefficient = -0.343, adds ratio = 0.71, Z=-1.52, p = 0.128). the odd ratio suggests that being married reduces the likelihood of private investment growth by 29% compared to being in another marital status in Table 6. Therefore, H1 is rejected.
PIG | coef | Odd ratio | St.d. Err | Z | P > Z |
|---|---|---|---|---|---|
GN | 5.131384 | 169.2512 | 95.91581 | 9.05 | 0.000 |
Age | .6599862 | 1.934766 | .4436816 | 2.88 | 0.004 |
EL | .1399949 | 1.150268 | .1305927 | 1.23 | 0.218 |
MS | − .3429598 | .7096668 | .1597402 | -1.52 | 0.128 |
ETH | − .2772113 | .7578943 | .1457644 | -1.44 | 0.149 |
CUL | − .6004322 | .5485745 | .2500118 | -1.32 | 0.188 |
NoD | − .4149397 | .6603801 | .0694289 | -3.95 | 0.000 |
Cons | -2.349626 | .0954049 | .1138996 | -1.97 | 0.049 |
Diagnostic tests | |||||
Log-likelihood − 85.75788 | |||||
Number of observations 309 | |||||
LR chi2(7) 223.22 | |||||
Prob > chi2 0.0000 | |||||
| Pseudo R2 0.5655 | |||||
Source: survey result 2024
Ethnicity (ETH)
Ethiopia is a highly diverse country, home to more than 86 ethnic groups. The nation’s federal governance system is structured around ethnicity, with administrative and political authority distributed among ethnically defined regions. One such region is the Afar region, which is governed predominantly by the Afar ethnic group. According to (Sahledengil & Amsalu, 2023), Ethiopia has experienced significant ethnic tensions over the past three decades, primarily attributed to the implementation of ethnic federalism, which has been a major root cause of the conflict. In this context, investors feel a greater sense of security when they invest within their regions and prefer conducting business within their ethnic groups. However, this variable is statistically insignificant (Z = -1.44, P = 0.149) and has a negative association with private investment growth in the study area. This indicates that because of investors' ethnicity, they are not facing any investing problems in the afar regional state and this model output is also supported by descriptive statistics in Table 2 that 84% of investors are non-afar ethnic groups in the study area. The coefficient for ethnicity is -0.277, with an odds ratio of 0.76. So, this finding consistent with related studies(Cassel et al., 2022) and (Hanna et al., 2010). Therefore, H1 is rejected.
Culture (CUL)
culture plays a crucial role in influencing private investment growth. (Kyriaki et al., 2020) insights that individualistic culture shapes investment behavior and decision making thereby influencing private investment growth. Table 6 analysis indicates that culture (CUL) has a negative but statistically insignificant relationship with private investment growth (coefficient = -0.600, odds ratio = 0.55, Z = -1.32, P = 0.188). The odds ratio suggests that individuals from certain cultural backgrounds are 45% less likely to experience private investment growth than others. This finding is supported by a previous study (Syed Khuram Shahzad, 2014) which found that cultural dimensions including religion and values, have an insignificant relationship with private investment. So, H1 is rejected.
Number of Dependents (NoD)
the influence of the Number of dependents on private investment growth quite varies from study to study. Some studies indicate that the number of dependents has a negative relationship and significant influence on private investment. while others found insignificant influences. So, the evidence is not inclusive universally. Table 6, shows that the number of dependents (NoD) has a significant negative relationship with private investment growth (coefficient = -0.415, odds ratio = 0.66, Z = -3.95, P < 0.001). The odds ratio indicates that for each additional dependent, the likelihood of private investment growth decreases by 34%, holding other factors constant. This leads us to conclude that having more dependents in an investor’s household limits financial resources or increases financial obligations, reducing the capacity for private investment growth. therefore, H1 is accepted, and it is concluded that more dependents in investors' households have a negative influence on private investment in the study area.
Conclusion and Recommendations
In this article, we investigate the influence of demographic determinants in pastoral and agro pastoral communities in afar regional state such as Gender, educational level, age, marital status, culture, ethnicity, and number of dependents on private investment growth by using both descriptive statistics and an econometrics model. Descriptive statistics mainly found that female participation is very low and needs more attention to ensure gender inclusiveness in the study area. From the model output, three variables such as Gender, age, and number of dependents are leading factors that have a direct influence on private investment growth in the Afar regional state. Cultural and ethnic factors were also found to influence private investment growth indirectly by affecting risk tolerance and investment preferences.
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Afar regional state is an economically viable zone 250 km from Djibouti and the nearest regional state to the Red Sea, particularly the port of Assab. So, the study recommends that enhancing private investment growth in the region needs holistic attention from both federal and regional governments. Especially conducting an awareness campaign to promote financial literacy for those who have lower-level education to bridge gaps in investment participation as well as growth. another area that needs policy intervention is gender-inclusive investment programs that address specific challenges faced by women, such as limited access to capital or resources. This could include mentorship programs, tax benefits, or subsidies for women investors.Direction for future study
The demographic variables of private investment were the sole objective of the current study. Prospective research could widen the scope to examine the investment environment in the tertiary (services, tourism), secondary (manufacturing, agro-processing), and primary (agricultural, mining) sectors. Given the abundance of natural resources in the Afar region, including gold, salt, and potash, there is a great deal of opportunity to look into the difficulties and investment opportunities unique to this industry.
Furthermore, Future research could also look at how government policies, infrastructure development, institutional frameworks, and environmental sustainability affect investment choices in the region's pastoral and agropastoral populations. Studies that compare various zones within the region or with nearby regions may also shed light on localized investment plans and best practices. Understanding what motivates sustainable investment in resource-rich but undeveloped regions like the Afar region may be further enhanced by investigating investor views, community involvement, and public-private partnerships.
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Author Contribution
Ali Mohammed Bodaya designed the study, collected the data, performed the analysis, and prepared the manuscript. Prashnata Sharma provided supervision, guided the methodology, reviewed the analysis, and improved the manuscript through critical revisions. Both authors read and approved the final version.
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Acknowledgement
I express my sincere gratitude to my research guide for your guidance, continuous support, and valuable feedback throughout this study. Your insights strengthened the quality of this thesis and helped me grow as a researcher.I thank the Indian Council for Cultural Relations for providing the scholarship, academic support, and the opportunity to pursue my doctoral studies in India. Your assistance made this work possible.I also appreciate the encouragement and cooperation of my department, colleagues, and all partici
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