By Admin 
Trump Releases Immigrant Households Data: US President Donald Trump on Sunday released a list of immigrants from around the world who he claims are taking advantage of welfare schemes and financial assistance within the US. Trump has shared these figures on his social media platform Truth Social.
The names of about 120 countries of the world are included in this list, but India’s name is not in it, which is considered to be the most attention-grabbing thing. The special thing is that India’s neighboring countries – Pakistan, Bhutan, Bangladesh, Nepal and China are clearly included in this list.
American released list of 120 countries
This chart released by Trump has come out at a time when America took a tough stance on Venezuela and took action to arrest its President Nicolas Maduro and his wife. This list was made public within just an hour of this action, due to which its political and diplomatic significance has become deeper.
Experts believe that this step is being seen in the context of Trump’s strict immigration policy and domestic politics, especially at a time when the debate about immigration and government spending is intense in America.
Actually, in this chart the assistance given by the US government to immigrants from different countries is shown in percentage form. But it does not clarify what kind of assistance this was – whether it was cash benefits, food assistance, health services or any other welfare scheme.
confusing list
Additionally, the chart does not provide information about the period for which this assistance was provided, how long immigrants received the benefit, and whether this assistance is still ongoing or has already been discontinued.
For this reason, experts and analysts are raising questions on the transparency and context of this data. He says that drawing conclusions only on the basis of percentage without clear timelines and scheme-wise details can be misleading. Overall, this chart conveys a political message but does not present a complete picture of the facts, leading to confusion regarding the interpretation of its data.
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