📄 social_science

Analysis of Measurement of Indicators of Sustainable Development Goals (SDGs) in the Context of Gulf Migration

This paper employs a mixed-method empirical study of Gulf migration in India to argue that integrating non-parametric statistical tests with qualitative socio-metric tools is essential for overcoming methodological limitations and effectively measuring Sustainable Development Goals (SDGs) within migration research.

Original authors: Mohammed Taukeer

Published 2026-06-30

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Mohammed Taukeer

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: Trying to Measure the Unmeasurable

Imagine you are trying to weigh a cloud. You have a very precise scale (standard statistics) that works great for rocks, apples, or bricks. But when you try to weigh a cloud, the scale gets confused because clouds are fluffy, change shape, and don't behave like solid objects.

This paper is about migration (people moving from one place to another, specifically from India to the Gulf countries). The author, Mohammed Taukeer, argues that migration is like that cloud. It's not just about money (which is easy to count); it's also about culture, feelings, and social habits.

The paper asks a big question: Can we use standard math tools to measure how "sustainable" (long-lasting and healthy) this migration is? The author says, "Not entirely." Standard math tools often miss the "cloudy" parts of human culture.

The Setup: The "Gulf" Connection

The study focuses on a specific group of people: workers from Uttar Pradesh (a state in Northern India) who went to work in the Gulf countries (like Saudi Arabia, UAE, etc.) and then came back home.

Think of this like a massive river of people flowing out of North India to the Gulf and then flowing back. The author wanted to see what happens to the families left behind when these workers return.

The Method: Why the Author Changed the Tools

Usually, researchers use Parametric Tests (like a rigid ruler) to analyze data. These tools demand that the data looks like a perfect "bell curve" (a smooth, symmetrical hill).

However, the author found that the data from these migrant families was messy and lopsided (skewed). It didn't fit the perfect bell curve.

The Analogy: Imagine trying to fit a square peg into a round hole. If you force it, you break the data.
The Solution: The author used Non-Parametric Tests. Think of this as using modeling clay instead of a rigid ruler. It's flexible. It can mold to the shape of the messy, real-world data without forcing it into a perfect shape.

The paper claims that for studying the cultural and social side of migration, this flexible "clay" approach is much better than the rigid "ruler."

What the Data Actually Showed

The author surveyed 180 returning workers and their families. Here is what they found, broken down simply:

1. The Money Story (Economic Indicators)

The Job: Most workers were semi-skilled (drivers, tailors, welders). A small group were highly skilled (managers, engineers).
The Pay Gap: There was a clear divide based on religion and job type.
- Muslim workers (who made up 96% of the sample) mostly worked as drivers or tailors. They earned a decent amount, but mostly in the lower-to-middle income brackets.
- Hindu workers (only 7 people in the sample) were all managers or engineers. They earned significantly more.
The Remittance (Money Sent Home): Workers sent a huge chunk of their salary back home (about 82%).
The Result: The money sent home acted like a fertilizer. It didn't just buy food; it changed the whole garden. Families spent much more on consumption after the migration compared to before.

2. The "Before and After" Test
The author compared how much families spent before the worker left and after they returned.

Before: Families spent about ₹7,148 a month.
After: Families spent about ₹15,527 a month.
The Conclusion: The difference was massive and statistically significant. The "clay" test (Wilcoxon Signed Rank) confirmed that the families' lives improved dramatically in terms of spending power.

3. The Cultural Story (Qualitative Findings)
This is where the "cloud" part comes in. The author looked at how people talked about the money.

"Riyal" as Culture: The workers didn't just call the money "cash." They called it "Riyal" (the currency of the Gulf). It became a symbol of status and success.
Myths and Beliefs: There were local sayings like "Paisa Bolta Hai" (The money speaks). This means the money itself had a voice and a power in the community.
Religious Impact: For the Muslim community, the money was often seen through the lens of Zakat (charity). The flow of money wasn't just economic; it was a spiritual and cultural cycle that strengthened the community's social fabric.

The Main Takeaway

The paper concludes that if you only look at migration through a standard economic lens (just counting dollars), you miss the whole story.

The Rigid Ruler (Standard Stats): Tells you how much money was earned.
The Flexible Clay (Non-Parametric + Qualitative): Tells you how that money changed the family's culture, their social status, and their daily life.

The author argues that to truly understand Sustainable Development Goals (SDGs) regarding migration, we need to use tools that can handle the messy, cultural, and human side of the story, not just the clean, mathematical side. We need to measure the "cloud" as well as the "rock."

Technical Summary: Analysis of Measurement of Indicators of Sustainable Development Goals (SDGs) in the Context of Gulf Migration

Problem Statement
The paper addresses the methodological and statistical challenges inherent in measuring Sustainable Development Goals (SDGs) within the context of migration, specifically focusing on Gulf migration from India. The central problem identified is the limitation of traditional econometric statistical tools in capturing the multidimensional, cultural, and socio-economic nuances of migration. While economic indicators (such as remittances) are easily quantifiable, the cultural and social dimensions of migration often lack normal distribution in empirical data, rendering standard parametric tests ineffective. The study posits that a "directionless hypothesis" exists regarding the insufficiency of econometric tools alone to measure the cultural aspects of migration, necessitating a shift toward socio-metric and non-parametric approaches to accurately assess sustainable development indicators.

Methodology
The research employs a mixed-method approach, combining quantitative and qualitative data collection and analysis, grounded in an empirical study of Gulf migration in rural and urban North India.

Sampling Strategy: The study utilized a multi-stage sampling technique.
- First Stage: Identification of North Indian states, with Uttar Pradesh (U.P.) selected as the primary focus due to its high volume of emigration clearances.
- Second Stage: Selection of the Lucknow district in U.P. as the specific study zone, ranked first in labor migration to Gulf countries.
- Third Stage: Selection of 10 census villages (rural) and 2 urban wards. From these, 180 migrant households were identified, containing at least one returned migrant who completed a two-year contract.
Data Collection: Primary data was gathered through field surveys conducted between November 2017 and March 2018.
- Quantitative: Structured schedules were used to collect data on income, remittances, and household consumption patterns (pre- and post-migration).
- Qualitative: Focus group interviews and participant observation were conducted to capture narratives, social values, and cultural perceptions.
Analytical Framework: The study utilizes an "input-output" model where income and remittances serve as independent variables, and household consumption patterns serve as dependent variables.
- Statistical Tests: Recognizing that the sample size ( $n=180$ $n = 180$ ) and the nature of the data did not meet normality assumptions (confirmed via Kolmogorov-Smirnov and Shapiro-Wilk tests), the study explicitly avoided parametric tests. Instead, it employed:
  - Spearman's Rho: For non-parametric correlation between income and remittances.
  - Simple Linear Regression: To model the relationship between income and remittances.
  - Wilcoxon Signed Rank Test: To compare household consumption expenditures before and after migration (paired non-parametric test).
- Qualitative Analysis: Content analysis, narratives, and case studies were used to interpret the socio-cultural impacts.

Key Results
The empirical analysis yielded several statistical and thematic findings:

Demographic and Occupational Profile: The sample consisted of 180 return migrants, predominantly Muslim (96%) and semi-skilled (88.5%). The average age was 29.8 years. A significant disparity was observed in income based on religion and skill level; Hindu migrants were exclusively in skilled managerial/engineering roles with higher average incomes, while Muslim migrants were largely in semi-skilled roles (drivers, tailors, welders).
Statistical Distribution: Both income and remittance data, as well as consumption expenditure data, were found to be non-normally distributed (Significance $p < 0.05$ ), validating the necessity of non-parametric testing.
Economic Correlations:
- A strong, statistically significant positive correlation ( $r = 0.901, p < 0.01$ ) was found between monthly income and monthly remittances.
- Regression analysis indicated that monthly income explains 86.8% of the variation in remittances ( $R^2 = 0.868$ ).
- On average, migrants remitted approximately 82% of their monthly income.
Consumption Patterns: The Wilcoxon Signed Rank Test revealed a statistically significant difference ( $Z = -11.652, p < 0.001$ ) in household consumption expenditures between the pre-migration and post-migration periods. Average monthly consumption increased from INR 7,148 to INR 15,527, indicating a marked improvement in socioeconomic conditions.
Qualitative Insights: The study identified a cultural phenomenon where remittances are referred to as "riyal" or "Saudi vala Paisa," symbolizing a structural transformation in the economic perception of migrant households. The inflow of remittances is deeply intertwined with Islamic economic practices (e.g., zakat), creating a "myth" of economic rationalism and luxury that drives further migration.

Key Contributions
The paper contributes to the field of migration studies and SDG measurement by:

Methodological Validation: Demonstrating the efficacy of non-parametric tests (Spearman's rho, Wilcoxon Signed Rank) in analyzing migration data that fails normality tests, arguing that these tools are essential for "socio-metric" analysis in social science.
Bridging Disciplines: Integrating econometric tools with socio-metric and cultural metrics to provide a more holistic view of sustainable migration, moving beyond purely economic indicators.
Contextual Specificity: Providing empirical evidence on the specific dynamics of Gulf migration from North India, highlighting the role of religion, skill segmentation, and cultural narratives in shaping economic outcomes.

Significance and Claims
The paper claims that the application of non-parametric tests and qualitative socio-metric tools is a relevant and necessary component for the sustainable study of migration. It argues that these methods fill knowledge gaps in understanding the "mute and invisible" cultural aspects of migration that traditional econometric tools often miss.

The authors conclude that this methodological approach offers a "realistic picture" of the multiple factors influencing migration under the SDG 2030 framework. The study suggests that understanding the nexus of migration and development requires acknowledging cultural pathways and social values, not just economic outputs. The findings imply that policy-oriented implications for sustainable migration must account for these cultural and statistical complexities to effectively address the contradictions and challenges faced by migrant communities. The paper does not propose new experimental applications but rather advocates for the adoption of this specific mixed-method, non-parametric framework to better measure the sustainable aspects of migration.