Analyzing the impact of remittance inflows on Tanzania’s social development and economic growth

Mushi, Hellena Mohamedy Article Analyzing the impact of remittance inflows on Tanzania’s social development and economic growth Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Mushi, Hellena Mohamedy (2024) : Analyzing the impact of remittance inflows on Tanzania’s social development and economic growth, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-12, https://doi.org/10.1080/23322039.2024.2345298 This Version is available at: https://hdl.handle.net/10419/321480 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Analyzing the impact of remittance inflows on Tanzania’s social development and economic growth Hellena Mohamedy Mushi To cite this article: Hellena Mohamedy Mushi (2024) Analyzing the impact of remittance inflows on Tanzania’s social development and economic growth, Cogent Economics & Finance, 12:1, 2345298, DOI: 10.1080/23322039.2024.2345298 To link to this article: https://doi.org/10.1080/23322039.2024.2345298 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 07 May 2024. Submit your article to this journal Article views: 1117 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Analyzing the impact of remittance inflows on Tanzania’s social development and economic growth Hellena Mohamedy Mushi Mbeya Campus College, Mzumbe University, Mbeya, Tanzania ABSTRACT This journal article analyses the impact of remittance (REM) inflows on Tanzania’s social development and economic growth (Egrow) spanning from 1990 to 2022 data from the World Bank (WB), International Monetary Fund (IMF) and World Economic Outlook (WEO) were used. The purpose of this article was to measure the relationship between EGrow and REM by using the following variables: Investment (INV), Population growth (POP Grow), Exchange rate (EXCHR), Government Expenditure (GOVEXP) and Inflation (INFL). The study aimed to examine the impact of these factors on REMs on the EGrow concept by integrating it into REM studies in Tanzania. The selected factors on REMs have not been included in most REM studies conducted in Tanzania; thus, their inclusion in the study expands our knowledge of REM utilization in Tanzania. Utilizing the Fourier Stationarity Test and applying the general to a specific technique, this research findings unveil REMs’positive and notable impact on Tanzania’s EGrow. Additionally, POP Grow, INV, EXCHR, GOVEXP and INFL exert a robust and substantial influence on REM. In conclusion, the empirical findings underscore the pivotal role of REMs in driving EGrow in Tanzania. The journal article recommended that decision-makers create proactive measures to encourage REM inflows. IMPACT STATEMENT This study investigates the dramatic impact of remittance inflows on Tanzania’s socioeconomic environment between 1990 and 2022. The study examines the complex relationship between remittances and key economic indicators such as investment, population growth, exchange rates, government spending, and inflation by combining data from reputable sources such as the World Bank, International Monetary Fund, and World Economic Outlook. By include these variables in the research, the study not only improves our understanding of remittance dynamics, but also sheds light on hitherto unknown aspects of their impact on Tanzanian economic growth. The findings show a significant and positive relationship between remittance inflows and economic progress, highlighting remittances’critical role in driving Tanzania’s growth trajectory. Furthermore, the study reveals the subtle linkages between remittances and other socioeconomic indicators, revealing their strong and significant impact on one another. This detailed analysis not only contributes to the academic conversation on remittances, but it also recommends policymakers to focus more on policies that encourage the diaspora to contribute to the country’s development. In this sense, the government should devise novel strategies to capture the diaspora’s funds. The study emphasizes the need of taking proactive efforts to promote and exploit remittance inflows for Tanzania’s long-term socioeconomic growth. ARTICLE HISTORY Received 25 December 2023 Revised 15 March 2024 Accepted 13 April 2024 KEYWORDS Remittances inflows; economic growth; Fourier; Tanzania REVIEWING EDITOR Yamini Sharma, Reviewer Selection Editor, Taylor and Francis, India SUBJECTS Economics; Finance; Business, Management and Accounting 1. Introduction Remittances (REMs), or money transfers to migrants’home countries, have increased significantly due to the significant growth in worldwide migration in recent decades. According to World Bank (WB) data from 2023, REMs are increasing globally when only formally documented transfers are considered. In particular, REMs to South Asia increased by 7.2% in 2023 to a significant $189 billion; however, this CONTACT Hellena Mohamedy Mushi [email protected] Mbeya Campus College, Mzumbe University, P. O. Box 6559, Mbeya, Tanzania ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2345298 https://doi.org/10.1080/23322039.2024.2345298 development trend appeared to be leveling off from the nearly 12% increase recorded in 2022. REM flows were projected to have climbed by almost 1.9% in 2023, totaling $54 billion, for Sub-Saharan Africa. According to projections, REM flows to the area are expected to rise by an additional 2.5% in 2024. An overall improvement in REM flows is predicted for the same year, 2024, with a projected rise of 2.1%, mostly due to an anticipated reversal in flows to Egypt. Regarding Latin America and the Caribbean, REM flows are anticipated to increase by 8% and reach $156 billion by 2023. A 3% rise in REMs to East Asia and the Pacific, totaling $133 billion, is anticipated in 2023. It is projected that REMs to lowand middle-income countries (LMICs) will total $669 billion in 2023. The dynamic character of REM patterns is highlighted by these numbers, which show differences in regional economic conditions and the global migration increase. Hence, the nexus between migrant REMs and economic growth (Egrow) has been a subject of substantial and persistent interest. Nevertheless, both theoretical and empirical investigations have yielded conflicting conclusions, preventing the establishment of a consensus on the impact of this relationship. Scholars, such as Ali Bare et al. (2022) and Tchekoumi and Nya (2023) assert a positive influence of REMs on EGrow, attributing this positive coefficient to stable policy environments. Rehman and Hysa (2021), employing financial development as a regressor, also discovered a positive correlation between REMs and EGrow. In a distinct vein, Abdulai (2023) suggests that the impact of REMs on EGrow is more pronounced in Ghana. Prempeh et al. (2023) establish that REMs conditionally foster growth in nations boasting developed financial systems and robust institutional environments, focusing on Ghana. Makina (2024) found a positive relationship between household consumption and REM in Lesotho, which portrayed that the more REM received, the lower the household consumption. A similar study conducted by Nik si c Radi c et al. (2023) in bibliometric found that it depends on the income level of a certain country; REM also influences positively EGrow. Saani et al. (2023) employed the ARDL method in data analysis and found that REM increases female unemployment in Ghana. Other scholars who found positive impacts on REM and EGrow were (Aslam et al., 2023 Barkat et al., 2023 Chen et al., 2023; Golder et al., 2023; Khan, 2023; Sutradhar, 2020). The synthesis of these findings highlights the complexity of the REM-EGrow relationship, with contextual factors, such as policy stability, financial development and institutional strength playing crucial roles. Consequently, the conclusion for the authors should acknowledge the diversity of outcomes and call for nuanced considerations of specific country contexts and the interplay of various factors in understanding the ramifications of REMs on EGrow. Conversely, Bajra (2021) and Al-Malki et al. (2022) present findings indicating a negative association between REMs and EGrow. They argue that REMs significantly diminish the labor effort of recipient households. However, this study faces criticism for not considering the endogenous nature of REMs, as Saani et al. (2023) pointed out. Similarly, Ofori et al. (2023) discovered a negative impact of REMs on EGrow. Additionally, Alsamara and Mrabet (2023) contend that the influx of REMs can lead to a real appreciation of the exchange rate (EXCHR), negatively affecting exports and potentially reducing trade openness, thereby impeding overall EGrow. Cazachevici et al. (2020) found that REM does not influence African EGrow while conducting a meta-analysis study. To contribute to the ongoing debate, this article suggests an empirical examination of the actual impact of REMs on EGrow. This is in line with Padhan et al. (2023), who posit a non-linear relationship between these variables, and Gapen et al. (2009), who propose no positive impact between workers’REM and EGrow. This study aims to contribute significantly to the empirical literature on the connection between migrant REMs and EGrow. Previous empirical research has indicated an uncertain or conflicting relationship and several factors may explain these divergent results. One factor is the omission of certain variables in the selection process (Adugna Chomen et al., 2023). Additionally, the broad scope of studies that amalgamate diverse countries without considering their specific characteristics may contribute to inconsistent findings (Matuzeviciute & Butkus, 2016). Such studies often overlook robustness tests that could highlight the influence of sub-regional membership, for instance. Moreover, there is merit in concentrating on one, given that such a goal necessitates not only a uniform policy but also a politically and economically stable institution. Lastly, the economic theory on EGrow is outdated, leading empirical studies to reflect the gaps in this theoretical framework. To address these issues, this research delves into the Fourier Stationarity Test of the relationship between migrant REMs and EGrow in Tanzania. Furthermore, this study opts for a sample of one country, Tanzania, 2 H. M. MUSHI covering the period from 1990 to 2022. The findings of this research indicate a positive significant relationship between migrant REMs and EGrow in Tanzania. The structure of this article is organized as follows: Section 2 provides a literature review, Section 3 outlines the methodological framework, Section 4 presents and discusses the results and finally, Section 5concludes the study. 2. Literature review Given the importance of REMs in international migration, assessing their impact on economic dynamics within Tanzania is critical. One important strategy is examining how REMs affect the recipient country’s EGrow. The question remains: can REMs solve Tanzania’s longstanding problem of low EGrow? Existing literature offers various opinions on how REMs influence recipient countries’EGrow. On the one hand, some academics, such as Fleming et al. (2017), Getish et al. (2020), Islam (2022), Matuzeviciute and Butkus (2016), Nik si c Radi c et al. (2023), Ofori and Grechyna (2021) and Zahra et al. (2007) claim that migrant REMs boost growth in receiving economies. Researchers, such as Umair et al. (2023), Sutradhar (2020), Tchekoumi and Nya (2023), Adugna Chomen et al. (2023) and Abdulai (2023), on the other hand, emphasize the negative consequences of REMs, claiming that they do not lead to positive EGrow due to a negative correlation between the two variables. According to them, there is no link between REMs and EGrow in these countries. The available theoretical literature detailing the many mechanisms via which REMs influence EGrow influences these contradictory empirical findings about the growth effects of REMs to some extent. Two main schools of thought have formed within the large body of literature regarding the growth effects of REMs: the migration pessimists and the migration optimists. The migration pessimists argue that REMs either have no effect on EGrow or have negative growth effects. In contrast, migration optimists present arguments in favor of the positive growth effects of REMs, highlighting indirect growth pathways through economic channels, such as increased savings, investment (INV) capital, human capital INVs, additional employment and the broader multiplier effects of consumption on aggregate demand and output. They contend that contrary to the migration optimists’claims, REMs are typically used for consumption rather than profitable INVs. Both schools of thought give opposing evidence addressing the growth effects of REMs through similar pathways, including consumption, human capital INV and labour supply. Given the disputed character of the material, reaching a firm judgment about the growth effects of REMs in Tanzania is difficult (Magai, 2020). As a result, it is critical to investigate the growth effects of REMs and answer the following research question: How do REMs impact the EGrow of receiving countries like Tanzania? The primary motivation for concentrating on the effects of REM inflows on social and economic development in Tanzania is the dearth of empirical knowledge and practitioner understanding regarding the relationship between REMs and economic development, inconsistent findings from earlier research and low-level records of emigrant inflows into Tanzania due to data shortages. Furthermore, there are a few published studies and data regarding the effect of REM inflows on Tanzania’s social and economic development. The journal article’s weak methodology, absence of multivariate analysis and sparse use of the Fourier in prior research on the impact of REM inflows on social and economic development in Tanzania are all excuses. But the Fourier Causality test has been applied in different contexts by numerous earlier studies (Aydin & Bozatli, 2023; David et al., 2023; Genc¸ et al., 2022; Qamruzzaman, 2023). The subsequent paragraphs address a more thorough examination of these arguments. Currently, no study has considered every factor that was chosen (government expenditure [GOVEXP], EXCHRs, population growth [POP Grow], INV, REMs and EGrow). This brings up the main topic of this journal article, which is whether these elements comprehensively impact Tanzania’s EGrow about REMs within a single research framework. In general, the majority of studies have focused on EGrow as the dependent (Adebayo et al., 2023; Adugna Chomen et al., 2023; Genevieve et al., 2023; Khan, 2023; Mamun & Kabir, 2023; Ofori et al., 2023; Padhan et al., 2023; Tabash et al., 2023; Tchekoumi & Nya, 2023; Umair et al., 2023). Furthermore, there is not much research that has focused on or studied REM in Tanzania (Eghan, 2022; Hansen, 2010,2012; Isoto & Kraybill, 2017; Magai, 2020; Musakwa & Odhiambo, 2022; Porter, 1980). COGENT ECONOMICS & FINANCE 3 Additionally, earlier research in this field was carried out in South Asia (Islam, 2022), Croatia (Depken et al., 2021), the UE countries (Golder et al., 2023; Mamun & Kabir, 2023; Soava et al., 2020), Asia (Tabash et al., 2023), Mexico, Indonesia, Nigeria and Turkey (Odugbesan et al., 2021), Africa (Genevieve et al., 2023), Organization of Islamic Cooperation (OIC) member (Kamalu et al., 2022), CEMAC zone (Tchekoumi & Nya, 2023), Pakistan (Abduvaliev & Bustillo, 2020; Mazher et al., 2020) Sri-Lanka (Aslam et al., 2023), India (Jayaraman & Makun, 2022; Khan, 2023), Bangladesh, India, Pakistan and Sri Lanka (Sutradhar, 2020), SubSaharan Africa (Adugna Chomen et al., 2023; Ofori & Grechyna, 2021), Guyana (Kumar, 2013), Gulf Cooperation Council (GCC) region (Al-Malki et al., 2022; Alsamara & Mrabet, 2023). Therefore, this study is timely and appropriate for the least developing nations like Tanzania. Few studies have looked at the factors influencing EGrow through REM inflows in developing nations (Barkat et al., 2023; Chen et al., 2023; Djeunankan et al., 2023; Eggoh et al., 2019), while the majority of previous studies examined factors like EGrow, REM, INV, POP Grow, EXCHR and GOVEXP that influence EGrow through REM inflows in developed and developing countries (e.g. GCC Region, Bangladesh, SriLanka, India and Pakistan,). Additionally, studies that included EXCHR (Genevieve et al., 2023; Magai, 2020), population-growth and GOVEXP (Genevieve et al., 2023), GOVEXP, inflation (INFL) and POP Grow (Ibrahim, 2022), INV (Getish et al., 2020; Kamalu et al., 2022). Similar to this, there EGrow through REM inflows. These factors include financial development (Chiwira, 2023; Ofori et al., 2023; Sidi & Meky, 2023) tourism, foreign direct INV (FDI) (Epaphra, 2016; Mwakabungu & Kauangal, 2023; Tabash et al., 2023; Waqas & Awan, 2023), renewable energy (Aydin & Bozatli, 2023) external debt (Jilenga et al., 2016; Kasidi & Said, 2013), outward FDI (Osarumwense & Igor, 2023), technological transfer (Osarumwense & Igor, 2023), industrialization (Lugina et al., 2022), natural resource (Ofori & Grechyna, 2021), agriculture (Epaphra & Mwakalasya, 2017; Lawal, 2022), tourism (Odhiambo, 2011), poverty (Musakwa & Odhiambo, 2022), trade openness (Asamoah et al., 2019), risk (Lumbila, 2016), innovation (Mtar & Belazreg, 2021), REM outflows (Al-Malki et al., 2022), exports (Ahmad et al., 2018) and digital financial inclusion (Chinoda & Kapingura, 2023). Nevertheless, the evidence is inconsistent regarding the other factors financial development, tourism, FDI, renewable energy, technological transfer and natural resources can affect EGrow through REM inflows (Aydin & Bozatli, 2023; Chiwira, 2023; Depken et al., 2021; Epaphra, 2016; Gapen et al., 2009; Ibrahim, 2022; Mwakabungu & Kauangal, 2023; Nik si c Radi c et al., 2023; Ofori et al., 2023; Salahuddin & Gow, 2015; Sidi & Meky, 2023; Tabash et al., 2023; Waqas & Awan, 2023). Additional factors, such as outward FDI, external debt, poverty, trade openness, risk, exports and digital financial inclusion, have also been found to affect EGrow through REM (Ahmad et al., 2018; Asamoah et al., 2019; Chinoda & Kapingura, 2023; Jilenga et al., 2016; Kasidi & Said, 2013; Lumbila, 2016; Mtar & Belazreg, 2021; Musakwa & Odhiambo, 2022; Osarumwense & Igor, 2023). Furthermore, most of these studies have been conducted in other nations and advanced economies. Additionally, the results of studies on the factors influencing EGrow through REM inflows could be more consistent. For example, some studies are found to be positively significant (Abdulai, 2023; Abduvaliev & Bustillo, 2020; Adugna Chomen et al., 2023; Aslam et al., 2023; Fleming et al., 2017; Genevieve et al., 2023; Getish et al., 2020; Golder et al., 2023; Isoto & Kraybill, 2017; Khan, 2023; Magai, 2020; Mamun & Kabir, 2023; Musakwa & Odhiambo, 2022; Odugbesan et al., 2021; Peprah et al., 2019; Tchekoumi & Nya, 2023) and negatively significant in other (Cazachevici et al., 2020; Depken et al., 2021; Eggoh et al., 2019; Eghan, 2022; Islam, 2022; Jayaraman & Makun, 2022; Ofori et al., 2023; Ofori & Grechyna, 2021; Soava et al., 2020; Sutradhar, 2020). More studies have been made possible because the earlier studies had inconsistent results, some good and some negative and some that showed no association. Furthermore, empirical studies have not examined the reasons behind Tanzania’s low REM record level (Magai, 2020). Neither research has provided evidence for the rationale behind Tanzania’s low REM record level, which led to the focus of this journal article on determining whether these factors influence EGrow through REM inflows in Tanzania holistically in one research framework through the Fourier Stationarity test. Furthermore, the Fourier Model must be utilized more when influencing EGrow through REM inflows (David et al., 2023; Genevieve et al., 2023). However, only a small number of earlier studies in other fields, including carbon emissions (Genc¸ et al., 2022), refugees and renewable energy (Aydin & Bozatli, 2023) and economic policy (Qamruzzaman, 2023). Therefore, using this Fourier stationarity test in Tanzania’s EGrow through REM inflows is important. 4 H. M. MUSHI Figure 1 shows the trends and variations in these three types of inflows over the specified years, providing valuable information about Tanzania’s economic relationships with both its diaspora and foreign entities. Figure 1 shows a consistent upward trend in REM flows to Tanzania. Since 1990, REMs have steadily increased, showcasing a growing influx of funds from Tanzanians living abroad to their home country. Notably, the figures demonstrate that REM flows have expanded to the extent that they now exceed the official development assistance (ODA) received by Tanzania. This suggests a significant economic impact from REMs, surpassing the traditional financial aid provided through ODA. The consistent rise in REM flows showcases the importance of these contributions to Tanzania’s economy, underlining their role in the country’s financial landscape. Figure 2 represents graphical representation of data set in Tanzania. Based on the aforementioned examination, it is evident that numerous pieces of reviewed literature still need to offer a definitive conclusion regarding the precise impact of REM inflows on the EGrow of nations. Furthermore, the influence of REMs on growth varies across different countries, making it inappropriate to generalize findings from such studies. Hence, it is necessary to undertake a study focusing on Tanzania as a case study to provide more context-specific insights. 3. Research methodology This study’s objective was to perform thorough empirical research to clarify Tanzania’s relationship between EGrow and REM inflow from 1990 to 2022. The WB, International Monetary Fund (IMF) and Figure 1. Remittances, foreign direct investment and official development assistance inflows to Tanzania. Source: IMF, World Economic Outlook, October 2023. Figure 2. Graphical representation of data set. COGENT ECONOMICS & FINANCE 5 World Economic Outlook (WEO) 2023 provided the statistics. We sought to identify patterns, trends and possible causal relationships between Tanzania’s overall EGrow and REM inflows by analyzing this relationship over a sizable period. The test for Fourier stationarity was employed. Stata was used to examine the data. I first specify a simple double log-linear Cobb-Douglass production function as: lnGDPt ¼aþb1lnREMt þb2ln INVt þb3ln EXCH Rt þb4ln INFLt þb5ln GOVEXP þb6ln POP GROW þet(1) 4. Presentations and discussions of the results Table 1 shows EGrow: measuring unit: Percentage (%) EGrow is represented as a percentage of the Gross Domestic Product (GDP), indicating the rate at which the economy has expanded. REM: measuring unit: US Dollars (US$). REM is measured in the current value of US dollars, representing the funds transferred from individuals working abroad to their home country. INV: measuring unit: Local Currency (e.g. Tanzanian Shillings [TZS]). INV is measured in the current value of the local currency, reflecting the amount of money invested within the country. POP Grow: measuring unit: Percentage (%) POP Grow is represented as a percentage, illustrating the annual growth rate of the population. EXCHR: Measuring unit: US Dollars (US$). The EXCHR is represented in US dollars, showing the value of the local currency compared to the US dollar. GOVEXP: measuring unit: Percentage (%). GOVEXP is a percentage of the GDP, indicating the portion of the GDP spent by the government. INFL: unit of measurement: Percentage (%): The annual rate at which the average level of prices for goods and services is rising and, as a result, the purchasing power of currency is declining, known as INFL. Table 2 shows descriptive statistics that offer a comprehensive overview of the distribution’s central tendency, dispersion and shape across seven variables: REM, E_GROW, EXCH_R, GOV_EXP, INFL, INV and POP_GROW. This relatively low figure in Table 2 indicates that REMs contribute marginally to the country’s growth even in the recent high emigration rates. 4.1. Unit-root test results Several economic variables were tested for stationarity using a Fisher-type unit-root test. For each variable under various test circumstances, including inverse chi-squared (24), inverse normal Z, inverse logit t(64), Land modified inverse chi-squared Pm, the table displays the test statistics and associated pvalues. Table 1. Variables and measuring units. Sr. No Variable names Abbreviations Measuring unit 1 Economic growth EGrow As % of GDP 2 Remittance REM In current US$ 3 Investment INV In the current local currency 4 Population growth POP Grow As annual % 5 Exchange rate EXCHR In current US$ 6 Government expenditure GOVEXP As % of GDP 7 Inflation INFL As annual % Table 2. Descriptive statistics. REM E_GROW EXCH_R GOV_EXP INFL INV POP_GROW Mean 1403586 10117.17 8.470821 0.076857 0.080459 0.214727 0.019832 Median 728727 9669.877 8.231263 0.069486 0.046351 0.221917 0.019501 Maximum 5101953 14728.74 17.18832 0.137748 0.272069 0.282174 0.027416 Minimum −225686.1 6947.593 1.338334 0.053247 0.022109 0.117051 0.011875 Std. Dev. 1414594 2675.284 4.555838 0.024625 0.065084 0.043622 0.002697 Skewness 0.522076 0.353778 0.27748 1.312863 1.286504 −0.405934 0.065167 Kurtosis 2.053411 1.624953 1.970881 3.626357 3.464129 2.131054 4.233944 Jarque-Bera 32.77372 39.45793 22.5566 120.2316 112.7905 23.33423 25.40347 Probability 0 0 0.000013 0 0 0.000009 0.000003 Sum 5.56E þ08 4006398 3354.445 30.43535 31.86188 85.03192 7.853665 Sum Sq. Dev. 7.90E þ14 2.83E þ09 8198.487 0.239528 1.673215 0.751629 0.002873 Observations 396 396 396 396 396 396 396 Note. REM: remittance; Egrow: economic growth; EXCHR: exchange rate; GOVEXP: government expenditure; INFL: inflation; INV: investment; POP Grow: population growth rate 6 H. M. MUSHI Table 3 results indicate that for variables, such as REM, economic growth (E_GROW), INFL and population growth rate (POP_GROW), the null hypothesis of a unit root is rejected, suggesting stationarity. Conversely, variables like exchange rate (EXCH_R), government expenditure (GOV_EXP) and INV do not provide sufficient evidence to reject the null hypothesis, implying potential non-stationarity in these cases. 4.2. Cointegration test Panel cointegration test results: Table 4 presents the results of the xtcointtest for panel cointegration among REM, E_GROW (economic growth), EXCH_R (exchange rate), GOV_EXP (government expenditure), INFL, INV and POP_GROW (population growth rate). The panel cointegration tests employ various Dickey–Fuller and Augmented Dickey–Fuller statistics. This is indicated in Table 4: Table 4 represents results that collectively suggest significant evidence in favor of panel cointegration among the considered economic variables. 4.3. Dynamic ordinary least squares (DOLS) panel data estimation The associations between the variables INV, POP_GROW (population growth), GOV_EXP (government expenditure), INFL and E_GROW (economic growth) were evaluated using the dynamic ordinary least squares (DOLS) panel data estimate technique shown in Table 5: In Table 5, the results suggest that INV, POP Grow, GOVEXP, INFL and EGrow are statistically significant predictors in the model. 4.4. Fully modified OLS (FMOLS) panel data estimation The fully modified OLS (FMOLS) panel data estimation was performed to examine the relationships between the variables INV, EXCH_R (exchange rate), POP_GROW (population growth), GOV_EXP (government expenditure), INFL and E_GROW (economic growth). This is represented in Table 6: Table 3. Fisher-type unit-root test. Variable Inverse chi-squared (24) Inverse normal ZInverse logit t(64) LModified inv. chi-squared Pm Statistic pValue Statistic pValue Statistic pValue Statistic pValue REM 3.2582 1.0000 4.0297 1.0000 3.8104 0.9998 −2.9938 0.9986 E_GROW 0.0249 1.0000 10.6671 1.0000 13.2264 1.0000 −3.4605 0.9997 EXCH_R 15.2294 0.9139 0.3083 0.6211 0.2757 0.6082 −1.2659 0.8972 GOV_EXP 25.2762 0.3909 −1.2888 0.0987 −1.1575 0.1257 0.1842 0.4269 INFL 63.0172 0.0000 −5.0269 0.0000 −4.9011 0.0000 5.6316 0.0000 INV 8.9190 0.9978 1.7505 0.9600 1.5756 0.9400 −2.1768 0.9853 POP_GROW 70.5215 0.0000 −5.5982 0.0000 −5.5513 0.0000 6.7148 0.0000 Note. REM: remittance; Egrow: economic growth; EXCHR: exchange rate; GOVEXP: government expenditure; INFL: inflation; INV: investment; POP Grow: population growth rate Table 4. xtcointtest Kao REM, E_GROW, EXCH_R, GOV_EXP, INFL, INV and POP_GROW. Statistic pValue Modified Dickey–Fuller t−14.6693 0.0000 Dickey–Fuller t−7.5046 0.0000 Augmented Dickey–Fuller t−6.9432 0.0000 Unadjusted modified Dickey–Fuller t−17.1643 0.0000 Unadjusted Dickey–Fuller t−7.8089 0.0000 Table 5. xtdolshm. Coefficient Std. err zp>z[95% conf Interval] INV −5961980 973,558.7 −6.12 0.000 −7,870,120 −4,053,840 POP_GROW 4.28e þ07 7,036,243 6.08 0.000 2.90e þ07 5.66e þ07 GOV_EXP −4.92e þ07 1,215,587 −40.45 0.000 −5.15e þ07 −4.68e þ07 INFL 2.18e þ07 424,541.7 51.25 0.000 2.09e þ07 2.26e þ07 E_GROW 616.6234 23.31031 26.45 0.000 0. 570.936 662.3107 COGENT ECONOMICS & FINANCE 7