diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
index a585cbed2551..3596ff105fd7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala
@@ -70,7 +70,7 @@ private[csv] object CSVInferSchema {
 
   def mergeRowTypes(first: Array[DataType], second: Array[DataType]): Array[DataType] = {
     first.zipAll(second, NullType, NullType).map { case (a, b) =>
-      findTightestCommonType(a, b).getOrElse(NullType)
+      compatibleType(a, b).getOrElse(NullType)
     }
   }
 
@@ -88,7 +88,7 @@ private[csv] object CSVInferSchema {
         case LongType => tryParseLong(field, options)
         case _: DecimalType =>
           // DecimalTypes have different precisions and scales, so we try to find the common type.
-          findTightestCommonType(typeSoFar, tryParseDecimal(field, options)).getOrElse(StringType)
+          compatibleType(typeSoFar, tryParseDecimal(field, options)).getOrElse(StringType)
         case DoubleType => tryParseDouble(field, options)
         case TimestampType => tryParseTimestamp(field, options)
         case BooleanType => tryParseBoolean(field, options)
@@ -172,35 +172,27 @@ private[csv] object CSVInferSchema {
     StringType
   }
 
-  private val numericPrecedence: IndexedSeq[DataType] = TypeCoercion.numericPrecedence
+  /**
+   * Returns the common data type given two input data types so that the return type
+   * is compatible with both input data types.
+   */
+  private def compatibleType(t1: DataType, t2: DataType): Option[DataType] = {
+    TypeCoercion.findTightestCommonType(t1, t2).orElse(findCompatibleTypeForCSV(t1, t2))
+  }
 
   /**
-   * Copied from internal Spark api
-   * [[org.apache.spark.sql.catalyst.analysis.TypeCoercion]]
+   * The following pattern matching represents additional type promotion rules that
+   * are CSV specific.
    */
-  val findTightestCommonType: (DataType, DataType) => Option[DataType] = {
-    case (t1, t2) if t1 == t2 => Some(t1)
-    case (NullType, t1) => Some(t1)
-    case (t1, NullType) => Some(t1)
+  private val findCompatibleTypeForCSV: (DataType, DataType) => Option[DataType] = {
     case (StringType, t2) => Some(StringType)
     case (t1, StringType) => Some(StringType)
 
-    // Promote numeric types to the highest of the two and all numeric types to unlimited decimal
-    case (t1, t2) if Seq(t1, t2).forall(numericPrecedence.contains) =>
-      val index = numericPrecedence.lastIndexWhere(t => t == t1 || t == t2)
-      Some(numericPrecedence(index))
-
-    // These two cases below deal with when `DecimalType` is larger than `IntegralType`.
-    case (t1: IntegralType, t2: DecimalType) if t2.isWiderThan(t1) =>
-      Some(t2)
-    case (t1: DecimalType, t2: IntegralType) if t1.isWiderThan(t2) =>
-      Some(t1)
-
     // These two cases below deal with when `IntegralType` is larger than `DecimalType`.
     case (t1: IntegralType, t2: DecimalType) =>
-      findTightestCommonType(DecimalType.forType(t1), t2)
+      compatibleType(DecimalType.forType(t1), t2)
     case (t1: DecimalType, t2: IntegralType) =>
-      findTightestCommonType(t1, DecimalType.forType(t2))
+      compatibleType(t1, DecimalType.forType(t2))
 
     // Double support larger range than fixed decimal, DecimalType.Maximum should be enough
     // in most case, also have better precision.
@@ -216,7 +208,6 @@ private[csv] object CSVInferSchema {
       } else {
         Some(DecimalType(range + scale, scale))
       }
-
     case _ => None
   }
 }