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CLAT UG Quantitative Techniques-Booster-Population, Demographic & Economic Analysis-Test 2.
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30 Minutes
❓
18 Questions
📌 Answers are locked once submitted — results and explanations appear at the end.
CLAT UG Quantitative Techniques-Booster-Population, Demographic & Economic Analysis-Test 2.
QUESTION 1 OF 18
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
QUESTION 2 OF 18
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
QUESTION 3 OF 18
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
QUESTION 4 OF 18
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
QUESTION 5 OF 18
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
QUESTION 6 OF 18
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
QUESTION 7 OF 18
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
QUESTION 8 OF 18
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
QUESTION 9 OF 18
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
QUESTION 10 OF 18
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
QUESTION 11 OF 18
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
QUESTION 12 OF 18
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
QUESTION 13 OF 18
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
QUESTION 14 OF 18
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
QUESTION 15 OF 18
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
QUESTION 16 OF 18
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
QUESTION 17 OF 18
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
QUESTION 18 OF 18
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
🏆
Test Complete!
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Answer Review
1
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
A
300 persons per square kilometre
B
350 persons per square kilometre
C
400 persons per square kilometre
D
450 persons per square kilometre
💡 Short Explanation
Coastshire had 2.4 million people across 6,000 square kilometres. Density = 2,400,000 ÷ 6,000 = 400 persons per square kilometre.
❓ Why Other Options Are Incorrect
A) 300 persons per square kilometre: This does not match the density obtained from 2,400,000 ÷ 6,000; the quotient is 400.
B) 350 persons per square kilometre: This is below the calculated density of 400 persons per square kilometre.
D) 450 persons per square kilometre: This is above the calculated density of 400 persons per square kilometre.
🎯 Strategy
Inference Mapping: Map Coastshire’s population of 2.4 million to its land area of 6,000 square kilometres and compute population ÷ area. The result, 400, matches Option C.
✅ Final Logic
Coastshire’s density is 2,400,000 ÷ 6,000 = 400 persons per square kilometre, so Option C is correct.
🧠 Memory Trick
Density = Population ÷ Area.
2
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
A
7.14 per cent
B
6.67 per cent
C
8.33 per cent
D
10 per cent
💡 Short Explanation
The increase was 3 million − 2.8 million = 0.2 million. Percentage increase = (0.2 ÷ 2.8 × 100) = approximately 7.14 per cent.
❓ Why Other Options Are Incorrect
B) 6.67 per cent: This would result from using the later population of 3 million as the base; percentage increase must use the 2020 value of 2.8 million.
C) 8.33 per cent: This exceeds the percentage obtained from the actual increase of 0.2 million on the 2.8 million base.
D) 10 per cent: A 10 per cent rise on 2.8 million would be 0.28 million, not the stated 0.2 million increase.
🎯 Strategy
Inference Mapping: Identify the increase as 0.2 million and map it to the earlier-year base of 2.8 million: 0.2 ÷ 2.8 × 100 ≈ 7.14 per cent, which matches Option A.
✅ Final Logic
Using 2020 as the base gives an increase of approximately 7.14 per cent, so Option A is correct.
🧠 Memory Trick
Percentage change uses the old value as base.
3
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
A
31 years
B
33 years
C
34 years
D
32.5 years
💡 Short Explanation
The ages in ascending order are 27, 31, 34 and 36. Median = ((31 + 34) ÷ 2) = 32.5 years.
❓ Why Other Options Are Incorrect
A) 31 years: This is only the lower of the two middle values after ordering the four ages; with an even number of observations, both middle values must be averaged.
B) 33 years: The average of the two middle ages, 31 and 34, is 32.5 rather than 33.
C) 34 years: This is only the upper of the two middle values and is not the median of an even-sized dataset.
🎯 Strategy
Elimination: Order the ages as 27, 31, 34 and 36, then eliminate choices that select only one middle value or an incorrect average. Averaging 31 and 34 gives 32.5, Option D.
✅ Final Logic
The median of four ordered values is the average of the middle two, giving 32.5 years.
🧠 Memory Trick
Even count: average the two middle values.
4
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
A
Northvale and Highplain
B
Riverland and Coastshire
C
Northvale and Riverland
D
Coastshire and Highplain
💡 Short Explanation
Riverland’s urban population = 60 per cent of 3 million = 1.8 million. Coastshire’s urban population = 75 per cent of 2.4 million = 1.8 million.
❓ Why Other Options Are Incorrect
A) Northvale and Highplain: Northvale has 45 per cent of 3.6 million = 1.62 million urban residents, while Highplain has 30 per cent of 3 million = 0.9 million.
C) Northvale and Riverland: Northvale has 1.62 million urban residents, whereas Riverland has 1.8 million.
D) Coastshire and Highplain: Coastshire has 1.8 million urban residents, while Highplain has 0.9 million.
🎯 Strategy
Inference Mapping: Convert each relevant urban percentage into an actual population before comparing. Riverland gives 1.8 million and Coastshire also gives 1.8 million, identifying Option B.
✅ Final Logic
Riverland and Coastshire each had 1.8 million urban residents, so Option B is correct.
🧠 Memory Trick
Convert percentages to actual numbers before comparing.
5
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
A
720,000
B
810,000
C
900,000
D
1,020,000
💡 Short Explanation
Children formed 30 per cent of Highplain’s population of 3 million. Number of children = 30 per cent of 3 million = 900,000.
❓ Why Other Options Are Incorrect
A) 720,000: This is 24 per cent of 3 million, not the stated 30 per cent child share in Highplain.
B) 810,000: This is 27 per cent of 3 million and therefore does not use Highplain’s stated 30 per cent share.
D) 1,020,000: This is 34 per cent of 3 million, exceeding the stated child proportion.
🎯 Strategy
Inference Mapping: Map Highplain’s 30 per cent child share to its 3 million population: 0.30 × 3,000,000 = 900,000, which matches Option C.
✅ Final Logic
Thirty per cent of Highplain’s 3 million residents equals 900,000 children.
🧠 Memory Trick
Part = Percentage × Total.
6
📄 Passage
The fictional Republic of Navira completed a demographic census in 2025. Its population was 12 million, compared with 11 million in 2020. For statistical analysis, the country was divided into four regions: Northvale, Riverland, Coastshire and Highplain. The census recorded each region’s population, land area, level of urbanisation and age composition.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
Northvale had 3.6 million residents spread over 12,000 square kilometres. Forty-five per cent lived in urban settlements. Children below fifteen formed 20 per cent of its population, working-age residents between fifteen and sixty-four formed 64 per cent, and people aged sixty-five or above formed 16 per cent. Northvale’s population in 2020 had been 3.2 million.
Riverland had 3 million residents and an area of 10,000 square kilometres. Its urban share was 60 per cent. Children formed 24 per cent, the working-age group 66 per cent and senior citizens 10 per cent. Riverland had contained 2.8 million residents in 2020.
Coastshire was geographically smaller, with 2.4 million people living across 6,000 square kilometres. It was the most urbanised region: 75 per cent of its population lived in towns and cities. Children accounted for 18 per cent, working-age residents 70 per cent and senior citizens 12 per cent. Its 2020 population had been 2 million.
Highplain contained 3 million residents across 15,000 square kilometres. Only 30 per cent were urban residents. Children formed 30 per cent of the population, working-age residents 62 per cent and senior citizens 8 per cent. Its total population had remained unchanged since 2020.
The median ages of Northvale, Riverland, Coastshire and Highplain were 34, 31, 36 and 27 years respectively. The census authority warned that a region’s total population should not be confused with its density, which depended on land area. It also stated that percentage shares must be converted into actual numbers before comparing age groups across regions. All demographic percentages in the passage refer to the relevant region’s 2025 population.
A
28:31
B
31:28
C
14:15
D
35:39
💡 Short Explanation
Coastshire’s working-age population = 70 per cent of 2.4 million = 1.68 million. Highplain’s working-age population = 62 per cent of 3 million = 1.86 million. Ratio = 1.68:1.86 = 28:31.
❓ Why Other Options Are Incorrect
B) 31:28: This reverses the required order; the question asks for Coastshire to Highplain, not Highplain to Coastshire.
C) 14:15: This is not equivalent to the calculated ratio 1.68:1.86, which simplifies to 28:31.
D) 35:39: This is also not equivalent to 1.68:1.86 after simplification.
🎯 Strategy
Elimination: Calculate the two working-age populations as 1.68 million and 1.86 million, retain the requested order, and simplify 168:186 to 28:31. This leaves Option A.
✅ Final Logic
The Coastshire-to-Highplain working-age ratio simplifies exactly to 28:31.
🧠 Memory Trick
Keep the ratio order exactly as asked.
7
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
A
450,000
B
540,000
C
630,000
D
600,000
💡 Short Explanation
Total employed persons = 3.6 million across six sectors. Average = 3.6 million ÷ 6 = 600,000 workers.
❓ Why Other Options Are Incorrect
A) 450,000: Multiplying this by six sectors gives only 2.7 million, not the stated 3.6 million total.
B) 540,000: Six sectors averaging 540,000 would total 3.24 million, below the stated workforce.
C) 630,000: Six sectors averaging 630,000 would total 3.78 million, above the stated workforce.
🎯 Strategy
Inference Mapping: Use the total employed workforce of 3.6 million and divide by the six sectors. The quotient is 600,000, matching Option D.
✅ Final Logic
The average workforce per sector is 3.6 million ÷ 6 = 600,000.
🧠 Memory Trick
Average = Total ÷ Number of groups.
8
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
A
90,000
B
120,000
C
150,000
D
180,000
💡 Short Explanation
Known formal workers totalled: 180,000 + 600,000 + 300,000 + 630,000 + 420,000 = 2,130,000. Missing construction figure = 2,250,000 − 2,130,000 = 120,000.
❓ Why Other Options Are Incorrect
A) 90,000: Adding 90,000 to the known formal-employment total of 2,130,000 gives only 2,220,000, not 2.25 million.
C) 150,000: This would raise the formal total to 2,280,000, exceeding the stated 2.25 million.
D) 180,000: This would raise the formal total to 2,310,000, also exceeding the stated total.
🎯 Strategy
Elimination: Subtract the known formal-sector total of 2,130,000 from the stated overall formal total of 2,250,000. The missing value is 120,000, so only Option B fits.
✅ Final Logic
Construction must account for the remaining 120,000 formal workers.
🧠 Memory Trick
Missing value = Grand total − Known total.
9
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
A
Agriculture
B
Manufacturing
C
Public and social services
D
Trade and transport
💡 Short Explanation
Women workers were: Agriculture (360,000), Manufacturing (252,000), Public and social services (378,000), and Trade and transport (243,000). Public and social services employed the most women.
❓ Why Other Options Are Incorrect
A) Agriculture: Women in agriculture numbered 40 per cent of 900,000 = 360,000, which is below 378,000 in public and social services.
B) Manufacturing: Women in manufacturing numbered 35 per cent of 720,000 = 252,000, below the highest figure.
D) Trade and transport: Women in trade and transport numbered 45 per cent of 540,000 = 243,000, below the highest figure.
🎯 Strategy
Inference Mapping: Convert each sector’s female percentage into an actual worker count. Public and social services has 60 per cent of 630,000 = 378,000 women, the largest among the options.
✅ Final Logic
Public and social services employed the greatest number of women at 378,000.
🧠 Memory Trick
Compare actual counts, not percentages alone.
10
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
A
12.5 per cent
B
15 per cent
C
16.67 per cent
D
20 per cent
💡 Short Explanation
Digital and business services employed 450,000 of 3.6 million workers. Percentage = (450,000 ÷ 3,600,000 × 100) = 12.5 per cent.
❓ Why Other Options Are Incorrect
B) 15 per cent: Fifteen per cent of 3.6 million would be 540,000 workers, more than the stated 450,000 in digital and business services.
C) 16.67 per cent: This corresponds to roughly 600,000 workers, not 450,000.
D) 20 per cent: Twenty per cent of 3.6 million would be 720,000 workers, far above the stated sector count.
🎯 Strategy
Elimination: Compute 450,000 ÷ 3,600,000 × 100 = 12.5 per cent and eliminate every option representing a larger share. Option A remains.
✅ Final Logic
Digital and business services accounted for exactly 12.5 per cent of employed workers.
🧠 Memory Trick
Share % = Part ÷ Total × 100.
11
📄 Passage
Navira’s Labour and Employment Survey examined 3.6 million employed persons in 2025. The survey divided them into six sectors. Agriculture employed 900,000 people, manufacturing 720,000, construction 360,000, trade and transport 540,000, public and social services 630,000, and digital and business services 450,000. These six sectors together represented the entire employed workforce covered by the survey.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
Women formed 40 per cent of agricultural workers, 35 per cent of manufacturing workers, 10 per cent of construction workers, 45 per cent of trade and transport workers, 60 per cent of public and social-service workers, and 50 per cent of digital and business-service workers. The remaining workers in each sector were men. The report advised readers to calculate actual worker numbers before comparing women’s employment across sectors because the sectors differed considerably in size.
The survey also classified employment as formal or informal. Formal employment included written contracts and recorded social-security contributions. Agriculture had 180,000 formal workers, manufacturing 600,000, trade and transport 300,000, public and social services 630,000, and digital and business services 420,000. Construction’s formal-worker figure was not printed in the regional summary. However, the survey stated that formal employment across all six sectors totalled 2.25 million people, allowing the omitted construction figure to be calculated.
The employed workforce had risen from 3.3 million in 2022 to 3.6 million in 2025. Digital and business services grew from 300,000 workers to 450,000, while agriculture declined from 990,000 to 900,000. Manufacturing increased from 660,000 to 720,000. The other three sectors together accounted for the remaining change.
The report distinguished employment from the labour force. An employed person performed paid or income-generating work during the survey period. The broader labour force also included unemployed people actively seeking work. Since the passage provides sectoral data only for employed persons, calculations involving the 3.6 million total should not include people who were outside employment or merely seeking work.
A
1:9
B
4:1
C
8:1
D
9:1
💡 Short Explanation
Women formed 10 per cent of 360,000 construction workers, or 36,000. Men formed the remaining 324,000. Ratio = 324,000:36,000 = 9:1.
❓ Why Other Options Are Incorrect
A) 1:9: This reverses the male-to-female order; construction has far more men than women.
B) 4:1: A 4:1 ratio would mean men are 80 per cent and women 20 per cent, inconsistent with the stated 10 per cent female share.
C) 8:1: An 8:1 ratio would imply women are one-ninth of the total, not the stated one-tenth.
🎯 Strategy
Inference Mapping: Women are 10 per cent of 360,000 = 36,000, so men are 324,000. Mapping these counts into male:female order gives 324,000:36,000 = 9:1, Option D.
✅ Final Logic
With 90 per cent men and 10 per cent women, the construction-worker ratio is 9:1.
🧠 Memory Trick
90% : 10% = 9 : 1.
12
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
A
8 per cent
B
10 per cent
C
12 per cent
D
15 per cent
💡 Short Explanation
Increase = ₹792 − ₹720 = ₹72. Percentage increase = (72 ÷ 720 × 100) = 10 per cent.
❓ Why Other Options Are Incorrect
A) 8 per cent: Eight per cent of ₹720 is ₹57.60, not the actual wage increase of ₹72.
C) 12 per cent: Twelve per cent of ₹720 is ₹86.40, which is larger than the actual increase.
D) 15 per cent: Fifteen per cent of ₹720 is ₹108, also larger than the actual ₹72 increase.
🎯 Strategy
Inference Mapping: Find the increase, ₹792 − ₹720 = ₹72, and map it to the original ₹720 wage: ₹72 ÷ ₹720 × 100 = 10 per cent, matching Option B.
✅ Final Logic
The ₹72 rise on the original ₹720 wage is exactly 10 per cent.
🧠 Memory Trick
Increase % = Increase ÷ Original × 100.
13
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
A
Approximately 79.9
B
Exactly 80.0
C
Approximately 81.2
D
Exactly 85.0
💡 Short Explanation
Weighted parity index = ((75 × 160) + (80 × 120) + (85 × 150)) ÷ (160 + 120 + 150) = 34,350 ÷ 430 = approximately 79.9.
❓ Why Other Options Are Incorrect
B) Exactly 80.0: The weighted calculation gives 34,350 ÷ 430 ≈ 79.9, not exactly 80.0.
C) Approximately 81.2: This is higher than the weighted result obtained from the stated female-worker weights.
D) Exactly 85.0: Eighty-five is only the services parity index; it ignores the lower agriculture and manufacturing indices and their female-worker weights.
🎯 Strategy
Inference Mapping: Weight each sector’s parity index by its female-worker count, add the weighted values, and divide by total female workers: 34,350 ÷ 430 ≈ 79.9. This identifies Option A.
✅ Final Logic
The female-worker-weighted parity index is approximately 79.9.
🧠 Memory Trick
Weighted index = Σ(index × weight) ÷ Σweights.
14
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
A
Agriculture, by ₹360 million
B
Manufacturing, by ₹540 million
C
Market services, by ₹1.665 billion
D
Market services, by ₹675 million
💡 Short Explanation
Women’s market-services wage bill = 150,000 × ₹1,020 × 25 = ₹3.825 billion. Women’s manufacturing wage bill = 120,000 × ₹720 × 25 = ₹2.16 billion. Difference = ₹3.825 billion − ₹2.16 billion = ₹1.665 billion.
❓ Why Other Options Are Incorrect
A) Agriculture, by ₹360 million: Agriculture’s women’s wage bill is ₹1.8 billion, which is lower than manufacturing’s ₹2.16 billion and therefore cannot be the highest.
B) Manufacturing, by ₹540 million: Manufacturing cannot exceed its own manufacturing wage bill, and market services has the larger total wage bill.
D) Market services, by ₹675 million: Market services is the correct highest sector, but ₹675 million is not the difference from manufacturing; the actual difference is ₹1.665 billion.
🎯 Strategy
Elimination: Calculate women’s monthly wage bills for the compared sectors: market services = ₹3.825 billion and manufacturing = ₹2.16 billion. Their difference is ₹1.665 billion, leaving only Option C.
✅ Final Logic
Market services had the highest women’s monthly wage bill and exceeded manufacturing by ₹1.665 billion.
🧠 Memory Trick
Wage bill = Workers × Daily wage × Days.
15
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
A
40 per cent
B
42.5 per cent
C
45 per cent
D
50 per cent
💡 Short Explanation
Market services employed 150,000 men and 150,000 women. Women’s share = (150,000 ÷ 300,000 × 100) = 50 per cent.
❓ Why Other Options Are Incorrect
A) 40 per cent: With 150,000 women out of 300,000 total workers, the female share is higher than 40 per cent.
B) 42.5 per cent: This does not equal 150,000 ÷ 300,000 × 100.
C) 45 per cent: This also understates the equal split between 150,000 men and 150,000 women.
🎯 Strategy
Inference Mapping: Combine 150,000 men and 150,000 women to get 300,000 total workers, then divide the female count by the total. The result is 50 per cent, Option D.
✅ Final Logic
Women were exactly half of the market-services workforce, so the answer is 50 per cent.
🧠 Memory Trick
Equal male and female counts mean 50% each.
16
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
A
43:57
B
57:43
C
19:14
D
4:3
💡 Short Explanation
Total men = 240,000 + 180,000 + 150,000 = 570,000. Total women = 160,000 + 120,000 + 150,000 = 430,000. Ratio = 570:430 = 57:43.
❓ Why Other Options Are Incorrect
A) 43:57: This reverses the required male-to-female order; 43 corresponds to women and 57 to men after simplification.
C) 19:14: This is not equivalent to 570:430; 19:14 would correspond to 570:420.
D) 4:3: This is only an approximation and is not the exact simplified ratio of 570:430.
🎯 Strategy
Elimination: Add all male workers to obtain 570,000 and all female workers to obtain 430,000. Simplify 570:430 by 10 to 57:43 and eliminate the reversed or non-equivalent ratios.
✅ Final Logic
The exact male-to-female ratio across the three sectors is 57:43.
🧠 Memory Trick
Add first, then simplify the ratio.
17
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
A
₹900
B
₹850
C
₹950
D
₹1,000
💡 Short Explanation
Average = ((₹600 + ₹900 + ₹1,200) ÷ 3) = ₹2,700 ÷ 3 = ₹900.
❓ Why Other Options Are Incorrect
B) ₹850: The three stated male wages total ₹2,700, and dividing by three does not give ₹850.
C) ₹950: A mean of ₹950 would require a total of ₹2,850, not the stated ₹2,700.
D) ₹1,000: A mean of ₹1,000 would require a total of ₹3,000, which exceeds the sum of the three wages.
🎯 Strategy
Inference Mapping: Add the three men’s daily wages, ₹600 + ₹900 + ₹1,200 = ₹2,700, then divide by three sectors. The result is ₹900, Option A.
✅ Final Logic
The simple average of the three male daily wages is ₹900.
🧠 Memory Trick
Simple mean = Sum ÷ Count.
18
📄 Passage
A wage survey in the Republic of Navira examined full-time workers in agriculture, manufacturing and market services. All workers covered by the survey were assumed to work 25 paid days each month. The survey reported worker numbers and average daily wages separately for men and women so that both individual pay gaps and total wage flows could be examined.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
Agriculture employed 240,000 men at an average daily wage of ₹600 and 160,000 women at ₹450. Manufacturing employed 180,000 men at ₹900 per day and 120,000 women at ₹720. Market services employed 150,000 men at ₹1,200 per day and 150,000 women at ₹1,020. Therefore, the female wage as a proportion of the male wage was 75 per cent in agriculture, 80 per cent in manufacturing and 85 per cent in services.
The report defined a wage-parity index by assigning the male wage in each sector an index value of 100 and expressing the corresponding female wage as an index against that base. To obtain a combined parity index for women, the sector indices were to be weighted by the number of female workers in each sector. This method gave greater influence to a sector employing more women than to a sector employing fewer women.
The survey also examined a proposed wage-equalisation policy. Under the proposal, women’s daily wage in each sector would rise to the existing male daily wage, while worker numbers and monthly paid days would remain unchanged. The additional monthly wage bill would therefore equal the daily wage gap multiplied by the number of women and by 25 days for each sector. No change in men’s wages was assumed.
Manufacturing employers had separately announced that the women’s daily wage would rise from ₹720 to ₹792 before any equalisation policy was introduced. The report treated this as a percentage increase measured against the original ₹720 wage. It cautioned that comparing only wage percentages could hide differences in worker numbers: a smaller daily gap in a large sector might create a greater aggregate cost than a larger gap affecting very few workers.
A
₹1.275 billion
B
₹1.665 billion
C
₹1.815 billion
D
₹2.025 billion
💡 Short Explanation
Additional monthly costs: Agriculture (₹150 × 160,000 × 25 = ₹600 million), Manufacturing (₹180 × 120,000 × 25 = ₹540 million), Services (₹180 × 150,000 × 25 = ₹675 million). Total = ₹600 million + ₹540 million + ₹675 million = ₹1.815 billion.
❓ Why Other Options Are Incorrect
A) ₹1.275 billion: This is below the sum of the three sector-specific equalisation costs of ₹600 million, ₹540 million and ₹675 million.
B) ₹1.665 billion: This figure does not equal the combined equalisation cost; it is lower than ₹600 million + ₹540 million + ₹675 million.
D) ₹2.025 billion: This exceeds the total produced by the stated wage gaps, female-worker counts and 25 paid days.
🎯 Strategy
Inference Mapping: For each sector, multiply the female daily wage gap by the number of women and by 25 days, then add the three costs: ₹600 million + ₹540 million + ₹675 million = ₹1.815 billion. This matches Option C.
✅ Final Logic
The three additional monthly wage costs sum to ₹1.815 billion, making Option C correct.
🧠 Memory Trick
Extra bill = Wage gap × Women × Days.