Employment in agriculture (% of total employment) (modeled ILO estimate) - Country Ranking
Definition: Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).
Source: International Labour Organization, ILOSTAT database. Data retrieved in September 2019.
See also: Thematic map, Time series comparison
Rank | Country | Value | Year |
---|---|---|---|
1 | Burundi | 86.21 | 2019 |
2 | Somalia | 80.28 | 2019 |
3 | Malawi | 76.36 | 2019 |
4 | Chad | 75.06 | 2019 |
5 | Niger | 72.54 | 2019 |
6 | Uganda | 72.13 | 2019 |
7 | Mozambique | 70.22 | 2019 |
8 | Central African Republic | 69.85 | 2019 |
9 | Ethiopia | 66.63 | 2019 |
10 | Zimbabwe | 66.19 | 2019 |
11 | Tanzania | 65.09 | 2019 |
12 | Nepal | 64.38 | 2019 |
13 | Dem. Rep. Congo | 64.30 | 2019 |
14 | Madagascar | 64.12 | 2019 |
15 | Eritrea | 63.12 | 2019 |
16 | Mali | 62.44 | 2019 |
17 | Rwanda | 62.29 | 2019 |
18 | Lao PDR | 61.44 | 2019 |
19 | Guinea | 60.65 | 2019 |
20 | Guinea-Bissau | 60.48 | 2019 |
21 | Vanuatu | 56.78 | 2019 |
22 | Papua New Guinea | 56.15 | 2019 |
23 | Bhutan | 55.78 | 2019 |
24 | Sierra Leone | 54.49 | 2019 |
25 | Kenya | 54.34 | 2019 |
26 | Angola | 50.73 | 2019 |
27 | Zambia | 49.64 | 2019 |
28 | Myanmar | 48.85 | 2019 |
29 | Tajikistan | 44.72 | 2019 |
30 | Lesotho | 44.30 | 2019 |
31 | Dem. People's Rep. Korea | 43.82 | 2019 |
32 | Cameroon | 43.49 | 2019 |
33 | Liberia | 42.62 | 2019 |
34 | India | 42.60 | 2019 |
35 | Afghanistan | 42.50 | 2019 |
36 | Côte d'Ivoire | 40.15 | 2019 |
37 | Equatorial Guinea | 39.51 | 2019 |
38 | Timor-Leste | 39.28 | 2019 |
39 | Sudan | 38.37 | 2019 |
40 | Bangladesh | 38.30 | 2019 |
41 | Benin | 38.27 | 2019 |
42 | Georgia | 38.15 | 2019 |
43 | Solomon Islands | 37.26 | 2019 |
44 | Vietnam | 37.22 | 2019 |
45 | Pakistan | 36.92 | 2019 |
46 | Albania | 36.42 | 2019 |
47 | Azerbaijan | 36.00 | 2019 |
48 | Nigeria | 34.97 | 2019 |
49 | Cambodia | 34.53 | 2019 |
50 | Comoros | 34.38 | 2019 |
51 | Congo | 33.53 | 2019 |
52 | Morocco | 33.25 | 2019 |
53 | Togo | 32.38 | 2019 |
54 | Thailand | 31.43 | 2019 |
55 | Guatemala | 31.30 | 2019 |
56 | Mauritania | 30.83 | 2019 |
57 | Nicaragua | 30.60 | 2019 |
58 | Bolivia | 30.54 | 2019 |
59 | Samoa | 30.21 | 2019 |
60 | Senegal | 30.10 | 2019 |
61 | Gabon | 29.96 | 2019 |
62 | Ghana | 29.75 | 2019 |
63 | Ecuador | 29.74 | 2019 |
64 | Honduras | 29.49 | 2019 |
65 | Haiti | 29.03 | 2019 |
66 | Indonesia | 28.50 | 2019 |
67 | Yemen | 27.55 | 2019 |
68 | Peru | 27.37 | 2019 |
69 | The Gambia | 27.03 | 2019 |
70 | Burkina Faso | 26.21 | 2019 |
71 | Uzbekistan | 25.71 | 2019 |
72 | China | 25.33 | 2019 |
73 | Mongolia | 25.32 | 2019 |
74 | Sri Lanka | 24.98 | 2019 |
75 | Djibouti | 24.55 | 2019 |
76 | Armenia | 24.05 | 2019 |
77 | Philippines | 22.86 | 2019 |
78 | Namibia | 21.85 | 2019 |
79 | Romania | 21.24 | 2019 |
80 | Moldova | 20.96 | 2019 |
81 | Turkmenistan | 20.68 | 2019 |
82 | Egypt | 20.62 | 2019 |
83 | Botswana | 19.90 | 2019 |
84 | Tonga | 19.37 | 2019 |
85 | Kyrgyz Republic | 19.32 | 2019 |
86 | São Tomé and Principe | 19.14 | 2019 |
87 | Paraguay | 18.72 | 2019 |
88 | Iraq | 18.27 | 2019 |
89 | Turkey | 18.11 | 2019 |
90 | Bosnia and Herzegovina | 17.96 | 2019 |
91 | Fiji | 17.61 | 2019 |
92 | Cuba | 17.40 | 2019 |
93 | Iran | 17.37 | 2019 |
94 | Belize | 16.80 | 2019 |
95 | Libya | 16.41 | 2019 |
96 | El Salvador | 16.29 | 2019 |
97 | Colombia | 15.77 | 2019 |
98 | Serbia | 15.61 | 2019 |
99 | Guyana | 15.44 | 2019 |
100 | Jamaica | 15.22 | 2019 |
101 | Kazakhstan | 14.86 | 2019 |
102 | Panama | 14.41 | 2019 |
103 | North Macedonia | 13.92 | 2019 |
104 | Ukraine | 13.82 | 2019 |
105 | Tunisia | 13.80 | 2019 |
106 | Mexico | 12.48 | 2019 |
107 | Eswatini | 12.15 | 2019 |
108 | Costa Rica | 11.97 | 2019 |
109 | Greece | 11.60 | 2019 |
110 | Lebanon | 11.32 | 2019 |
111 | Belarus | 11.06 | 2019 |
112 | Cabo Verde | 10.60 | 2019 |
113 | Malaysia | 10.28 | 2019 |
114 | Syrian Arab Republic | 10.13 | 2019 |
115 | St. Vincent and the Grenadines | 10.08 | 2019 |
116 | St. Lucia | 9.97 | 2019 |
117 | Algeria | 9.60 | 2019 |
118 | Poland | 9.15 | 2019 |
119 | Brazil | 9.08 | 2019 |
120 | Chile | 8.98 | 2019 |
121 | Dominican Republic | 8.78 | 2019 |
122 | Uruguay | 8.41 | 2019 |
123 | Suriname | 8.08 | 2019 |
124 | Venezuela | 7.86 | 2019 |
125 | Latvia | 7.29 | 2019 |
126 | Montenegro | 7.15 | 2019 |
127 | Bulgaria | 6.62 | 2019 |
128 | Lithuania | 6.44 | 2019 |
129 | Croatia | 6.19 | 2019 |
130 | Mauritius | 5.97 | 2019 |
131 | New Zealand | 5.84 | 2019 |
132 | Russia | 5.83 | 2019 |
133 | Portugal | 5.50 | 2019 |
134 | South Africa | 5.28 | 2019 |
135 | Korea | 5.14 | 2019 |
136 | Hungary | 4.72 | 2019 |
137 | Ireland | 4.43 | 2019 |
138 | Slovenia | 4.28 | 2019 |
139 | Iceland | 4.04 | 2019 |
140 | Spain | 4.03 | 2019 |
141 | Oman | 3.99 | 2019 |
142 | Italy | 3.89 | 2019 |
143 | Finland | 3.78 | 2019 |
144 | Austria | 3.66 | 2019 |
145 | Japan | 3.38 | 2019 |
146 | Estonia | 3.17 | 2019 |
147 | Trinidad and Tobago | 3.03 | 2019 |
148 | Slovak Republic | 2.79 | 2019 |
149 | Czech Republic | 2.66 | 2019 |
150 | Barbados | 2.65 | 2019 |
151 | Switzerland | 2.59 | 2019 |
152 | Australia | 2.56 | 2019 |
153 | France | 2.53 | 2019 |
154 | Jordan | 2.47 | 2019 |
155 | Saudi Arabia | 2.41 | 2019 |
155 | Cyprus | 2.41 | 2019 |
157 | Denmark | 2.22 | 2019 |
158 | The Bahamas | 2.20 | 2019 |
159 | Netherlands | 2.08 | 2019 |
160 | Norway | 2.04 | 2019 |
161 | Brunei | 1.95 | 2019 |
162 | New Caledonia | 1.87 | 2019 |
163 | Kuwait | 1.78 | 2019 |
164 | Sweden | 1.69 | 2019 |
165 | Canada | 1.51 | 2019 |
166 | United Arab Emirates | 1.39 | 2019 |
167 | United States | 1.36 | 2019 |
168 | Germany | 1.21 | 2019 |
169 | Qatar | 1.17 | 2019 |
170 | Puerto Rico | 1.09 | 2019 |
171 | United Kingdom | 1.05 | 2019 |
172 | Malta | 1.02 | 2019 |
173 | Bahrain | 0.94 | 2019 |
174 | Israel | 0.92 | 2019 |
174 | Belgium | 0.92 | 2019 |
176 | Luxembourg | 0.68 | 2019 |
177 | Macao SAR, China | 0.40 | 2019 |
178 | Hong Kong SAR, China | 0.17 | 2019 |
179 | Argentina | 0.06 | 2019 |
180 | Singapore | 0.03 | 2019 |
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Development Relevance: Sectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labour flows from agriculture and other labour-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas. The breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment. Segregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.
Limitations and Exceptions: There are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source. Countries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries. The ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata.
Statistical Concept and Methodology: The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity. The series is part of the ILO estimates and is harmonized to ensure comparability across countries and over time by accounting for differences in data source, scope of coverage, methodology, and other country-specific factors. The estimates are based mainly on nationally representative labor force surveys, with other sources (population censuses and nationally reported estimates) used only when no survey data are available.
Aggregation method: Weighted average
Periodicity: Annual